Massachusetts Institute of Technology, 2012. — 1067 p. ISBN: 0262018020, 978-0262018029. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and...
Wiley, 2015. — 360 p. — ISBN: 1118961749, 9781118961742 Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this...
Cambridge University Press, 2012. — 396 p. — ISBN: 978-1107096394. As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to...
Machine Learning Mastery, 2016. — 179 p. The Python ecosystem with scikit-learn and pandas is required for operational machine learning. Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it directly to production. In this mega Ebook written in the friendly Machine Learning Mastery...
Machine Learning Mastery, 2016. — 223 p. — ISBN: N\A Do you want to do machine learning using R, but you’re having trouble getting started? In this post you will complete your first machine learning project using R. In this step-by-step tutorial you will: Download and install R and get the most useful package for machine learning in R. Load a dataset and understand it’s...
Packt Publishing, 2016. — 653 p. — ISBN10: 178439968X. — ISBN13: 978-1784399689 This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. About This Book Fully-coded working examples using a wide...
McGraw-Hill, 1997. 414 p. — ISBN: 0070428077. This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning. The field of machine learning is concerned with the question...
New York: Jason Brownlee., 2018. — 212 p. Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to, you will finally cut through the equations, Greek letters, and confusion, and discover the...
Andriy Burkov, 2019. — 152 p. Is this book for you? You will enjoy the book if you are: - a software engineer or a scientist who wants to become a machine learning engineer or a data scientist - a data scientist trying to stay on the edge of the state-of-the-art and deepen their ML expertise - a manager who wants to feel confident while talking about AI with engineers and...
N.-Y.: Chapman and Hall/CRC, 2014. - 457 p. A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer...
Morgan Kaufmann, 2011. - 630 p. - ISBN: 0123748569 (Third Edition) Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by...
3rd ed. — MIT Press, 2014. — 640 p. — ISBN: 0262028182, 9780262028189 The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be...
Packt Publishing, 2017. — 450 p. — ISBN: 978-1-78829-575-8. True PDF Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain...
Manning Publications, 2012. — 384 p. — ISBN: 1617290181, 9781617290183. Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting,...
2nd Edition. — Packt Publishing, 2015. — 454 p. — ISBN: 978-1-78439-390-8 Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R — a cross-platform, zero-cost statistical programming environment — there...
John Wiley & Sons, 2016. — 435 p. — (For Dummies). — ISBN: 1119245516, 9781119245513. Your no-nonsense guide to making sense of machine learning. Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on...
Apress, 2018. — 545 p. — ISBN: 978-1-4842-3206-4. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner....
2016. — 162 p. The book was designed to teach developers about machine learning algorithms. It includes both procedural descriptions of machine learning algorithms and step-by-step tutorials that show exactly how to plug-in numbers into the various equations and exactly what numbers to expect on the other side.
Academic Press, 2015, -1072 p. Machine Learning is a name that is gaining popularity as an umbrella formethods that have been studied and developed for many decades in different scientific communities and under different names, such as Statistical Learning, Statistical Signal Processing, Pattern Recognition, Adaptive Signal Processing, Image Processing and Analysis, System...
2005 by Elsevier Inc. Part I Machine learning tools and techniques. What’s it all about? nput: Concepts, instances, and attributes. Output: Knowledge representation. Algorithms: The basic methods. Credibility: Evaluating what’s been learned. mplementations: Real machine learning schemes. Transformations: Engineering the input and output. Moving on: Extensions and applications....
Springer, 2016. — 364 p. Data science is one of the emerging fields in the twenty-first century. This field has been created to address the big data problems encountered in the day-to-day operations of many industries, including financial sectors, academic institutions, information technology divisions, health care companies, and government organizations. One of the important...
2nd Edition. Wiley-Interscience, 2001. - 738 p. The ease with which we recognize a face, understand spoken words, read handwritten characters, identify our car keys in our pocket by feel, and decide whether an apple is ripe by its smell belies the astoundingly complex processes that underlie these acts of pattern recognition. Pattern recognition — the act of taking in raw data...
The MIT Press – 2012, 432 p. ISBN: 026201825X, 9780262018258 This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts...
Packt Publishing, 2013. — 396 p. — ISBN: 1782162143, 9781782162148 Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R — a cross-platform, zero-cost statistical programming environment — there has never...
O’Reilly, 2019. — 362 p. — ISBN: 1492035645. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled...
Packt Publishing, 2018. — 576 p. — ISBN: 978-1788621113. Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their...
2nd Edition. — MIT Press, 2010. — 581 p. Machine learning is programming computers to optimize a performance criterion using example data or past experience. We need learning in cases where we cannot directly write a computer program to solve a given problem, but need example data or experience. One case where learning is necessary is when human expertise does not exist, or...
ORM, 2012. - 304 p. - ISBN: 1449303714 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning — a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on...
CreateSpace Independent Publishing, 2017. — 490 p. — ISBN/ASIN: B06XC21FZV. Machine Learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to produce reliable, repeatable decisions and...
InTech, 2010, -446 p. The goal of this book is to present the key algorithms, theory and applications that from the core of machine learning. Learning is a fundamental activity. It is the process of constructing a model from complex world. And it is also the prerequisite for the performance of any new activity and, later, for the improvement in this performance. Machine...
Packt Publishing, 2017. — 376 p. — ISBN: 978-1788398435. Extract patterns and knowledge from your data in easy way using MatLAB. Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MatLAB Understand how your data works and identify...
Manning, 2016. — 427 p. — ISBN: 1617291927, 9781617291920 Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling,...
Packt, 2018. — 503 p. The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market,...
Packt Publishing, 2017. — 449 p. — ISBN: 978-1785889622. True PDF Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in...
3rd Edition. — O’Reilly Media, Inc., 2022. — 850 p. — ISBN: 978-1-098-12597-4. Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This best-selling book uses concrete...
New York: Apress, 2018. — 712 p. Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As...
MIT Press, 2015. — 599 p. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most...
Massachusetts Institute of Technology, 2002. — 645 p. — ISBN: 0262194759, 978-0262194754. Series: Adaptive Computation and Machine Learning series. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a...
Packt Publishing, 2019. — 456 p. — ISBN: 1789136364. A guide to advances in machine learning for financial professionals, with working Python code. Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind...
Packt Publishing, 2018. — 566 p. — ISBN: 1788390040. Practical, hands-on solutions in Python to overcome any problem in Machine Learning Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to...
Scatterplot Press, 2017. — 155 p. — ISBN: 1549617214. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey...
Apress, 2019. — 384 p. Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you'll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance,...
InTech, 2009, -430 p. Machine Learning is often referred to as a branch of artificial intelligence which deals with the design and the development of algorithms and techniques that help machines to learn. Hence, it is closely related to various scientific domains as Optimization, Vision, Robotic and Control, Theoretical Computer Science, etc. Based on this, Machine Learning can...
Morgan Kaufmann Publishers, 2016. — 524 p. — ISBN: 9780128021217 Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech...
Addison-Wesley Professional, 2019. — 280 p. Foundational Hands-On Skills for Succeeding with Real Data Science Projects This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique...
Massachusetts Institute of Technology, Neural Information Processing Series, 2012. — 512 p. ISBN: 026201646X, 978-0262016469. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from...
Wiley, 2019. — 307 p. — ISBN: 978-1-119-54567-5. Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines....
Springer, 2010. — 736 p. Machine learning (ML) is one of the most fruitful fields of research currently, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems. From a technological point of view, the world has changed at an unexpected pace; one of the consequences is that it is possible to use high-quality and fast...
New York: Jason Brownlee., 2018. — 291 p. — ISBN N\A. The information contained within this eBook is strictly for educational purposes. If you wish to apply ideas contained in this eBook, you are taking full responsibility for your actions. The author has made every e_ort to ensure the accuracy of the information within this book was correct at time of publication. The author...
InTech, 2010, — 374 p. The purpose of this book is to provide an up-to-data and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call Learning tasks, as we use the word in daily life. It is also broad enough to encompass computer that improve from experience in quite...
Packt Publishing, 2017. — 270 p. Your one-stop guide to becoming a Machine Learning expert. Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by...
CRC Press – 2010, 479 p. ISBN: 9781439815915 Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks,...
Abiprod Pty, 2018. — 125 p. — ISBN: 1718095031. Are you excited about Artificial Intelligence and want to get started?Are you excited about Machine Learning and want to learn how to implement in Python? The book below is the answer. Given the large amounts of data we use everyday; whether it is in the web, supermarkets, social media etc. analysis of data has become integral to...
Packt Publishing, 2018. — 500 p. — ISBN: 1509304444. Unleash Google's Cloud Platform to build, train and optimize machine learning models Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at...
Apress, 2018. - 362 p. - ISBN: 1484235630 Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get...
AI Sciences LLC, 2018. — 184 p. — ASIN B07G4M16JF. Are you thinking of learning more about Machine Learning using Python? This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you...
BPB Publications, 2018. — 266 p. — ISBN: 9386551934. This book provides the concept of machine learning with mathematical explanation and programming examples. Every chapter starts with fundamentals of the technique and working example on the real-world dataset. Along with the advice on applying algorithms, each technique is provided with advantages and disadvantages on the...
O’Reilly Media, 2016. — 300 p. — ISBN10: 149196460X, ISBN13: 978-1491964606. Machine learning has finally come of age. With H 2 O software, you can perform machine learning and data analysis using a simple open-source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H 2 0 with only...
Apress, 2017. — 335 p. — ISBN: 978-1-4842-2249-2. This book is a comprehensive guide to machine learning with worked examples in MatLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially...
Cambridge: Cambridge University Press, 2020. — 398 p. — ISBN 110845514X. Machine learning is the latest in a long line of attempts to distill human knowledge and reasoning into a form that is suitable for constructing machines and engineering automated systems. As machine learning becomes more ubiquitous and its software packages become easier to use, it is natural and...
Imperial College Press, 2014. — 260 p. — ISBN: 1848168136, 9781848168138 his volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is,...
CreateSpace Independent Publishing, 2018. — 106. — ISBN: 1719528403. Do You Want to Become An Expert Of Machine Learning? Start Getting this Book and Follow My Step by Step Explanations! This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a...
Wiley, 2014. — 408 p. — ISBN: 1118889061 Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML...
Cambridge University Press, 2011, 492 p., 144 b/w illus. ISBN: 9780521192248. This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset...
O’Reilly Media, 2017. — 201 p. — ISBN: 978-1491924136 True PDF Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with...
InTech, 2010, -288 p. In recent years many successful machine learning applications have been developed, ranging from data mining programs that learn to detect fraudulent credit card transactions, to information filtering systems that learn user’s reading preferences, to autonomous vehicles that learn to drive on public highways. At the same time, machine learning techniques...
Machine Learning Mastery, 2018. — 115 p. Welcome to XGBoost With Python. This book is your guide to fast gradient boosting in Python. You will discover the XGBoost Python library for gradient boosting and how to use it to develop and evaluate gradient boosting models. In this book you will discover the techniques, recipes and skills with XGBoost that you can then bring to your...
Leanpub, 2019. — 250 p. Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about...
Springer, 2008, -610 p. The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a unified style. In a nutshell, we identify at least three...
AI Sciences LLC, 2018. — 132 p. — ASIN B07GMVP3WP. Are you thinking of learning more about Machine Learning using Python? (For Beginners) This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be...
MIT, 2016. — 206 p. — ISBN: 9780262529518 Alpaydın Ethem Machine Learning: The New AI Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect...
Data Sciences, 2017. — 136 p. — ISBN: 1985670968. The overall aim of this book is to help you understand the application of machine learning techniques with Python. Machine learning is a field of Artificial Intelligence that uses algorithms to learn from data and make predictions. This means that we can feed data into an algorithm, and use it to make predictions about what...
Amazon Digital Services LLC, 2019. — 175 p. Python with machine learning is increasing day-to-day popularity. Python is currently a widely used programming language for machine learning. Like the use of python in professional work, it is also growing reputation in the academic sector. In Coursera, various online courses, like programming language for machine learning, the use...
Manning Publications Co., 2021. — 513 p. — ISBN: 978-1617295911. Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble...
Addison-Wesley Professional, 2019. — 592 p. — (Addison-Wesley Data & Analytics Series). — ISBN: 978-0134845623. The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyonewill help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an...
2018. — 246 p. — ISBN: 1727338944. Have you ever wanted to learn how to better use your data? Are you interested in the works of machine learning? If you answered yes to these questions, then this book is for you. Machine Learning and Deep learning are powerful data tools that can help improve businesses. In this book, you will learn: Neural networks Machine learning Python and...
ITexLi, 2017. — 446 p. — ISBN: 953-307-033-1. The goal of this book is to present the key algorithms, theory and applications that from the core of machine learning. Learning is a fundamental activity. It is the process of constructing a model from complex world. And it is also the prerequisite for the performance of any new activity and, later, for the improvement in this...
Independently published, 2019. — 369 p. — ISBN: 978-1686500237, 1686500238. Your Guide to Getting Ahead with Python! Today, several commercial apps and research projects make use of machine learning, but this field is not only meant for big companies with extensive research teams, a beginner can get started, too. Machine Learning came into prominence in the 1990s, when...
Apress, 2018. — 355 p. — ISBN: 978-1-4842-2987-3. Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the...
Manning Publications, 2018. — 224 p. Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera About the Technology If you're building machine learning models to be...
Packt Publishing, 2017. — 570 p. — ISBN: 9781785280511. Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a...
O’Reilly, 2018. - 226 p. - ISBN: 1491976446 Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, youll first start to learn with regression modeling and then move into more advanced...
Globaltech NTC, 2018. — 144 p. You are interested in becoming a machine learning expert but don't know where to start from? Don't worry you don't need a big boring and expensive Textbook. This book is the best guide for you. Here are the reasons: The author has explored everything about machine learning and deep learning right from the basics. A simple language has been...
AI Sciences, 2018. — 190 p. — ISBN13: 978-1724417503. This book is for you. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. This book and the accompanying examples, you would be well suited to tackle problems which pique your interests...
K.Taylor, 2017. — 413 p. Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning...
Packt, 2016. — 358 p. — ISBN: 978-1-78588-840-3. Based on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0 Set up your business application to start using machine learning techniques Familiarize the user with some of the more common .NET libraries for machine learning Implement several common machine learning techniques Evaluate, optimize and adjust machine learning...
Packt Publishing, 2018. — 350 p. — ISBN: 1788996402. Power up your C# and .NET applications with exciting machine learning models and modular projects Key Features Produce classification, regression, association and clustering models Expand your understanding of machine learning and C# Get the grips of C# packages such as Accord.net, LiveCharts, Deedle Book Description Machine...
Manning Publications, 2018. — 275 p. — ISBN: 9781617293337. — MEAP version 11 Manning Early Access Program (MEAP). MEAP began February 2016. Publication in February 2018 (estimated). Reactive Machine Learning Systems teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. This example-rich guide...
The MIT Press, 2017. — 624 p. — ISBN: 026203641X. The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage,...
New York: John Wiley & Sons, 2019. — 350 p. — ISBN: 1119548217. Cyber-solutions to real-world business problems! Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe....
Sebastopol (CA.): O’Reilly Media, 2019. — 380 p. Many industry experts consider unsupervised learning the next AI frontier, one that may hold the key to general artificial intelligence. Armed with the conceptual knowledge in this book, data scientists and machine learning practitioners will learn hands-on how to apply unsupervised learning to large unlabeled datasets using...
Packt Publishing, 2018. — 282 p. Automate data and model pipelines for faster machine learning applications AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create...
Oliver Theobald, 2017. — 52 p. The manner in which computers are now able to mimic human thinking to process information is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the modern age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the...
2018.-75 p.- ISBN-10 B07FKZN93N Machine Learning algorithms for beginners - data management and analytics for approaching deep learning and neural networks from scratch How can a beginner approach machine learning with Python from scratch? Why exactly is machine learning such a hot topic right now in the business world? Ahmed Ph. Abbasi will lead you from being a complete...
InTech, 2009, -386 p. Even since computers were invented many decades ago, many researchers have been trying to understand how human beings learn and many interesting paradigms and approaches towards emulating human learning abilities have been proposed. The ability of learning is one of the central features of human intelligence, which makes it an important ingredient in both...
Springer, 2016. — 345 p. — ISBN: 978-3-319-17289-7, e-ISBN: 978-3-319-17290-3. Machine learning stands as an important research area that aims at developing computational methods capable of improving their performances with previously acquired experiences. Although a large amount of machine learning techniques has been proposed and successfully applied in real systems, there...
Wiley, 2018. — 352 p. — ISBN: 1119439191. An introduction to machine learning that includes the fundamental techniques, methods, and applications. Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author an expert in the field presents fundamental ideas, terminology, and...
Amazon Digital Services, 2017. — 61 p. This book is an introduction to basic machine learning and artificial intelligence. It gives you a list of applications, and also a few examples of the different types of machine learning. Here's What You'll Learn in this Book: – Introduction to Machine Learning – Different Applications of Machine Learning – Introduction to Statistics for...
O’Reilly, 2015. — 59 p. Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming. Now you have help. With this O’Reilly report, machine-learning expert Alice Zheng takes you through...
2nd edition. — Morgan & Claypool, 2018. — 207 p. — ISBN: 978-1681733043. Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a...
2nd Edition. — O’Reilly, 2019. — 856 p. - ISBN: 1492032646 Final Edition Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book...
Springer, 2018. — 373 p. — ISBN: 3319949888. This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental...
Springer, 1983. — 565. The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learning processes is of great significance to fields concerned with understanding intelligence. Such fields include cognitive science, artificial intelligence, information science, pattern recognition,...
New York: Springer, 2019. — 433 p. This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering,...
CreateSpace Independent Publishing Platform, 2013. — 520 p. — ISBN: 978-1489507716. This book serves two purposes. First, it teaches the importance of using sophisticated yet accessible statistical methods to evaluate a trading system before it is put to real-world use. In order to accommodate readers having limited mathematical background, these techniques are illustrated with...
Amazon Digital Services LLC, 2019. — 135 p. — ASIN B07NRVL21R. There are different processes that are included within the meaning of the term "learning." If you refer to a dictionary and look up the meaning of learning, then you will come across different phrases like "to gain knowledge, understanding of or skill, through study, experience or instruction," and "the change in...
O’Reilly Media, 2014. — 234 p. — ISBN: 1449374069, 9781449374068 Learn how to apply test-driven development (TDD) to machine-learning algorithms — and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms,...
CreateSpace Independent Publishing Platform, 2017. — 56 p. The Ultimate Beginners Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple.From smart bulbs to self-driving cars, intelligent machines are becoming ever more prevalent in our day to day lives. The underpinning of this technology is called machine learning, and is the same basic concept...
Apress, 2019. — 702 p. — ISBN: 1484244699. Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform....
Apress, 2019. - 365 p. - ISBN: 9781484236574 Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future...
Springer, 2019. — 219 p. — ISBN: 3030053172. This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid...
Boca Raton: Chapman & Hall/CRC Press, 2004. — 365 p. — (Computer science and data analysis). — ISBN: 1-58488-387-1. Bayesian Artificial Intelligence, in our understanding, is the incorporation of Bayesian inferentialmethods in the development of a software architecture for an Artificial Intelligence (AI). We believe that important ingredients of such an architecture will be...
Springer, 2008, -256 p. The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. With first papers dating back to the 1960’s, DAR is a mature but still growing research field with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely...
McGraw-Hill Education, 2019. — 656 p. — ISBN: 978-1260456844, 1260456846. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning...
Packt Publishing, 2016. — 298 p. — ISBN: 978-1-78588-660-7. — True PDF. Explore the web and make smarter predictions using Python. Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book...
Springer, 2006. — 656. Feature selection and model selection are two major elements in machine learning. Both feature selection and model selection are inherently multi-objective optimization problems where more than one objective has to be optimized. For example in feature selection, minimization of the number of features and maximization of feature quality are two common...
2nd edition. — Boston: The MIT Press, 2018. — 505 p. What is machine learning? What kind of problems can be tackled using machine learning? Some standard learning tasks Learning stages Learning scenarios Generalization The PAC learning model Guarantees for finite hypothesis sets — consistent case Guarantees for finite hypothesis sets — inconsistent case Deterministic versus...
3rd ed. — CRC Press, 2017. — 691 p. — ISBN: 1498797601. The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. is a compilation of new and creative data mining techniques,...
Morgan & Claypool, 2018. — 169 p. The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many...
O’Reilly Media, 2018. — 45 p. — ISBN: 9781492033158. Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate but also makes their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers, affecting...
Independently published, 2019. - 435 p. - ISBN: 1710263423. Created with the beginner in mind , this powerful bundle delves into the fundamentals behind Python and Machine Learning , from basic code and mathematical formulas to complex neural networks and ensemble modeling. Inside, you’ll discover everything you need to know to get started with Python and Machine Learning, and...
Wiley, 2012. — 298 p. There are always difficulties in making machines that learn from experience. Complete information is not always available — or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic...
Packt Publishing, 2018. - 378 p. - ISBN: 1788838297 Leverage the power of Apple's Core ML to create smart iOS apps Key Features Explore the concepts of machine learning and Apple's Core ML APIs Use Core ML to understand and transform images and videos Exploit the power of using CNN and RNN in iOS applications Book Description Core ML is a popular framework by Apple, with APIs...
Chapman and Hall/CRC, 2017. — 364 p. — (Machine Learning & Pattern Recognition). — ISBN10: 1138626783; ISBN13: 978-1138626782. Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove...
MIT Press, 2017. — 225 p. — (MIT Press essential knowledge). — ISBN: 9780262529518. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we dont yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data...
Packt Publishing, 2015. — 190 p. — ISBN: 978-1-78439-908-5. Control your machine learning algorithms using test-driven development to achieve quantifiable milestones Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences. Machine...
IBM Limited Edition. - John Wiley & Sons, Inc., 2018. – 75 p. Machine learning is having a dramatic impact on the way software is designed so that it can keep pace with business change. Machine learning is so dramatic because it helps you use data to drive business rules and logic. How is this different? With traditional software development models, programmers wrote logic...
De Gruyter, 2019. — 537 p. — ISBN: 978-3-11-049950-6. This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artificial...
Independently published, 2019. — 128 p. — ISBN: 1092554556. A baby learns to crawl, walk and then run. we are in the crawling stage when it comes to appllying Machine Learning. Just about anyone with the slightest bit of interest in modern technology is looking to learn more about Machine Learning. This innovative new form of computer programming is the primary tool that makes...
Springer, 2019. — 155 p. — ISBN: 981133028X This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis....
New York: Springer, 2018. — 173 p. This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information...
Morgan & Claypool Publishers, 2019. — 175 p. — (Synthesis Lectures on Data Management). — ISBN10: 1681734982, 13 978-1681734989. Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many...
Cambridge University Press, 2011. — 424 p. — ISBN: 0521196000, 9780521196000 The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines...
CreateSpace, 2017. — 266 p. Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning
LazyProgrammer, 2016. — 66 p. In a real-world environment, you can imagine that a robot or an artificial intelligence won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’d want that robot to be able to explore the world on its own, and learn things just by looking for patterns. Think about the large amounts of data being collected...
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning. — Morgan and Claypool Publishers, 2009. — 116 p. ISBN: 978-1598295474, e-ISBN: 978-1598295481. Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning...
Third Edition. — 2018. — 442 p. This book implements many common Machine Learning algorithms in equivalent R and Python. This is the 3rd edition of the book. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques...
O’Reilly Media, 2022. — 386 p. — ISBN13: 978-1-098-10796-3. Machine learning systems are both complex and unique. They are complex because they consist of many different components and involve many different stakeholders. They are unique because they are data-dependent, and data varies wildly from one use case to the next. This book takes a holistic approach to designing...
Birmingham: Packt Publishing, 2019. — 403 p. — ISBN: 1789954924. Explore the exciting world of machine learning with the fastest growing technology in the world ! Key Features Understand various machine learning concepts with real-world examples Implement a supervised machine learning pipeline from data ingestion to validation Gain insights into how you can use machine learning...
EPFL Press, 2009, -380 p. The book is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense, machine learning can be considered a subfield of artificial intelligence; the subject is mainly concerned with the development of techniques and algorithms that allow computers to learn from data. In this...
Cambridge Univercity Press, 2014. — 603 p. — ISBN: 9781107024960 Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the...
De Gruyter, 2018. — 183 p. — (Frontiers In Computational Intelligence). — ISBN: 978-3110550320. This volume comprises six well-versed contributed chapters devoted to report the latest findings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is...
Independently published, 2018. — 79 p. Do you want to impress the processes that you are working on? Do you want to make your machines more intelligent? If your answer to any of those questions is yes, then you have come to the right place. This book is a sequel to the book titled 'Machine Learning: A Step-by-Step guide.' In the first book, you gathered information on what...
Packt, 2019. — 360 p. — ISBN: 9781838645359. This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. Key Features Roll up your sleeves and start programming AI models No math, data science, or machine learning background required Packed with hands-on examples, illustrations, and clear step-by-step instructions 5...
Springer International Publishing, Switzerland, 2017. — 229 p. — (Fluid Mechanics and Its Applications. Volume 116) — ISBN10: 331940623X. Guides the reader from the control of simple dynamical systems to real-world experiments assisted by ample supplementary material Contains interviews with leading experts in the field Offers extensive color figures with clear explanations...
New Jersey: Wiley, 2009. - 267 p. This book has been written to provide an introduction to this important class of machine learning algorithms with a minimum of technical background in order to make this material as widely accessible as possible. With the exception of some basic notions in calculus and probability theory, the book is completely self-contained. Important...
Packt Publishing, 2016. — 257 p. — ISBN10: 1784396583, 13 978-1784396589. If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life. You should be...
Wiley, 2018. — 268 p. — (Wiley series in probability and statistics). — ISBN: 978-1-119-43898-4. Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author — an expert in the field — presents fundamental ideas, terminology, and techniques for solving applied problems in...
Springer, 2018. — 317 p. — ISBN: 978-3319730394. This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General...
Cham: Springer International Publishing, 2019. — 206 p. — ISBN: 978-3-319-99223-5. This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational...
Springer, 2019. — 263 p. — ISBN: 978-3030157289, 3030157288. Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts...
Springer, 2014, -120 p. As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to...
Oakville (Canada): Arcler Press, 2019. — 347 p. — ISBN: 177407365X. Fundamentals of Machine Learning discusses the basics of Python, use of Python in computing and provides a general outlook on machine learning. This book provides an insight into concepts such as linear regression with one variable, linear algebra, and linear regression with multiple inputs . The classification...
Apress, 2018. — 545 p. — ISBN: 978-1-4842-3206-4. Code files only! Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a...
Independently publ., 2019. — 137 p. — ISBN13: 978-1097417117. Machine learning can be described as a form of statistical analysis, often even utilizing well-known and familiar techniques, that has bit of a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the...
Packt Publishing, 2017. — 304 p. — ISBN: 1785882104. Build simple, maintainable, and easy to deploy machine learning applications The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but...
Khaled Bayoudh, 2017. — 166 p. There are a growing number of people who are seeking to understand the main concepts of Machine/Deep Learning and what powers them up. And if you are of these people, then this book is for you! This book discusses the Machine/Deep Learning algorithms, methods, concepts, functions and code that make Deep Neural Networks such as Convolutional Neural...
New York: Morgan & Claypool, 2017. — 128 p. Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the...
O’Reilly Media, 2020. — 168 p. — (Early Release). Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools...
Cambridge: The MIT Press, 2001. — 568 p. This is the first textbook that provides a thorough, comprehensive and unified introduction to the field of learning from experimental data and soft computing. Support vector machines (SVMs) and neural networks (NNs) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed...
Amazon Digital Services LLC, 2018. — 54 p. — (Machine Learning for Beginners Book 1). Welcome to the world of machine learning! Are you looking for a foundational book to get you started with the basic concepts of Machine Learning? My book will explain you the basic concepts in ways that are easy to understand. Once you’ve read this book, you’ll have a solid grasp on the core...
Amazon Digital Services LLC, 2018. — 129 p. — ASIN B07F2NYDTH. Smart homes, self-driving cars, Siri, Alexa - some typical examples of how machine learning and artificial intelligence have become part of our daily life. Wouldn't it be cool to understand the concepts behind these complex topics? This book teaches you how to integrate machine learning into your apps. We're going...
UK, King’s College London, 2018. — 237 p. This monograph is an attempt to offer a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment, encompassing also more recent developments and pointers to the liter- ature for further study. This monograph aims at providing an introduction to key concepts, algorithms, and...
Manning Publications, 2018. — 253 p. Machine learning applications autonomously reason about data at massive scale. It’s important that they remain responsive in the face of failure and changes in load. But machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring. teaches readers how to implement reactive...
Amazon Digital Services LLC, 2016. — 63 p. Artificial intelligence is changing our lives in ways we need to understand. Algorithms govern how we find information, how we learn, how we move, how we buy, what we buy, how we stay healthy, how we meet, whom we meet, how we are treated and what we are treated with. Marketing, analytics, diagnostics, manufacturing, driving,...
Packt Publishing, 2016. — 298 p. — ISBN: 978-1-78588-660-7. Explore the web and make smarter predictions using Python Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps...
Springer, 2018. - 525 p. - ISBN: 9811312796 This book presents high-quality papers from an international forum for research on computational approaches to learning. It includes current research and findings from various research labs, universities and institutions that may lead to development of marketable products. It also provides solid support for these findings in the form...
CRC Press, 2012, -316 p. "Cost-Sensitive Machine Learning" is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It...
Springer, 2015. — 296 p. — ISBN: 9783319200095 This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers,...
Cham: Springer International Publishing, 2019. — 273 p. — ISBN: 978-3-319-98566-4. This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also...
Amazon Digital Services LLC, 2018. — 50 p. Excited about computer science, machine learning and programming? This book is the ultimate guide to Python programming for beginners and intermediates! Considering that Python is used by big tech companies like Google, Nokia, IBM, Yahoo!, Pinterest, Disney and many others, it makes a lot of sense to prioritize learning Python if you...
Apress, 2018. - 392 p. - ISBN: 1484239504 Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You...
Morgan Kaufmann, 2014. — 323 p. — ISBN: 0123985374, 9780123985378 The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction....
NY: InfoQ, 2017. — 36 p. Machine learning has long powered many products we interact with daily — from "intelligent" assistants like Apple's Siri and Google Now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook. More recently, machine learning has entered the public consciousness because of advances...
Leanpub, 2019. — 259 p. Deep Learning has revolutionized the Machine Learning field. Python tools like Scikit-Learn, Pandas, TensorFlow, and Keras allows you to develop applications powered by Machine Learning. This book is written for you, the Machine Learning practitioner . Every chapter describes a problem and a solution that you'll encounter in your Machine Learning...
Second Edition. — Wiley, 2020. — 356 p. — ISBN: 978-1-119-56201-6. This book, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark — a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark...
New York: Chapman and Hall/CRC, 2019. — 335 p. Machine Learning is known under many names such as Machine Learning, Artificial Intelligence, Pattern Recognition, Data Mining, Data Assimilation, and Big Data, to list but a few. It developed in many areas of science, such as in physics, engineering, computer science, and mathematics in parallel and independently. For example it...
Boston: Kluwer Academic Publishers, 2004. - 220 p. Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of...
New York: Sybex, 2019. — 510 p. Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do...
Springer, 2019. — 137 p. — ISBN: 3030170756. This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part , the authors propose algorithms to find local minima in nonconvex...
Springer, 2019. — 552 p. — (Learning and Analytics in Intelligent Systems 01). — ISBN: 978-3-030-15627-5. This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies....
Publication details not specified. — 291 p. Edited by Tomasz Pawlak to match requirements of course of Applications of Computational Intelligence Methods at Poznan University of Technology, Faculty of Computing. As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying...
Springer, 2020. — 261 p. — ISBN: 3030266214. This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of...
Cambridge University Press, 2018. — 162 p. — ISBN: 978-1-107-18458-9. This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced...
Packt Publishing, 2022. — 741 p. — ISBN: 9781801819312. PyTorch book of the bestselling and widely acclaimed Python Machine Learning series expanded to include transformers, XGBoost, and graph neural networks Key Features: Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine...
Packt, 2019. - 664 p. - ISBN: 9781838827878 Your one-stop guide to working with the browser-based JavaScript library for training and deploying machine learning models effectively Key Features Build, train and run machine learning models in the browser using TensorFlow.js A handy guide to make your web applications smart from scratch using example-based approach Use flexible...
Springer, 2019, — 320 p. The volume of data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. In addition, these models must take into account many constraints: (pseudo)...
2018. —374 p. — ISBN: 1138492698. This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine - from storing enormous amounts of biological data to solving complex biological problems and enhancing treatment of various grave diseases.
O’Reilly, 2020. — 303 p. Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practices,...
Springer, 2018 - 257 p. Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyze large and continuously changing software systems. These require new software analysis techniques...
Ciml.info; TODO, 2015. — 227 p. This is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It's focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could...
Springer, 2016. — 120 p. — (Studies in Big Data 20). — ISBN: 9783319333816, 9783319333830 This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction...
Packt Publishing, 2018. — 2014 p. — ISBN: 1789138132. !Code files only START READING Book Description Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity About This Book Learn how to apply core machine learning concepts to your games with Unity Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them...
Ellis Horwood Publishing. 1994. - 298 p. The aim of this book is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems. Before describing the contents, we first need to define what we mean by classification, give...
Packt Publishing, 2018. — 282 p. Automate data and model pipelines for faster machine learning applications AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create...
NY: InfoQ, 2018. — 40 p. Machine learning (ML) and deep-learning technologies like Apache Spark, Flink, Microsoft CNTK, TensorFlow, and Caffe brought data analytics to the developer community. Whether it's classifying 2 million sales products received from over 700 multinational retailers for the "Love the Sales" website, building awareness of hindsight bias with customers at...
Academic Press, 2019. — 449 p. — ISBN: 978-0-12-817444-9. This book presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive...
Idea Group, 2007, -384 p. Machine learning is the study of how to build computer programs that improve their performance at some task through experience. The hallmark of machine learning is that it results in an improved ability to make better decisions. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly,...
Jorge Brasil, 2023. — 151 p. Has the abstract nature of linear algebra ever left you overwhelmed? Do you yearn to unlock the essence of machine learning but are bogged down by the intricacy of mathematics? Dive into a realm where linear algebra unfolds not just as numerical operations, but as a powerful story. A story intertwined with the magic of machine learning, making sense...
Springer, 2010. — 474 p. — ISBN: 978-3642141249, e-ISBN: 978-3642141256. The topic of preferences is a new branch of machine learning and data mining , and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals....
New York: Springer, 2007. — 279 p. — ISBN: 978-0-387-69938-7, e-ISBN: 978-0-387-69942-4. The objectives we set for this book are two-fold: (1) bring together those important machine learning techniques that are particularly powerful and effective for modeling multimedia data; and (2) showcase their applications to common tasks of multimedia content analysis. Multimedia data,...
New York: Springer, 2020. — 202 p. This book discusses one of the major applications of Artificial Iintelligence (AI): the use of Machine Learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book...
Morgan & Claypool Publishers, 2019. — 160 p. — ISBN: 9781681734408, 9781681734415, 9781681734422. The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often,...
Springer, 2016. — 251 p. — (Studies in Big Data 7). — ISBN: 9783319069371, 9783319069388 The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms...
John Wiley & Sons, Inc., 2019. — 366 p. — ISBN: 978-81-265-7990-7. This book is written to provide a strong foundation in Machine Learning using Python libraries by providing real-life case studies and examples. It covers topics such as Foundations of Machine Learning, Introduction to Python, Descriptive Analytics and Predictive Analytics. Advanced Machine Learning concepts...
O’Reilly Media, 2025. — 416 p. — ISBN-13: 978-1-098-14924-6. Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and...
Morgan Kaufmann, 2019. — 327 p. — ISBN: 978-0-12-814623-1. This book presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general...
Springer, 2014. — 162 p. — ISBN: 978-3-658-04936-2, e-ISBN: 978-3-658-04937-9. Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. However, motor skills are not easy to learn – babies require several month to develop proper grasping skills. Learning motor skills is also an active research topic in robotics. However,...
Apress, 2019. — 637 p. — ISBN: 1484253159. Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data...
New York: Springer, 2018. — 485 p. With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for...
Amazon.com Services LLC., 2020. — 146 p. Become the master of machine learning with this powerful guide. Do you want to know more about neural networks? Have you heard of machine learning, but you’re not sure where to begin? Written with the beginner in mind, this detailed guide breaks down everything you need to know about deep and machine learning in a simple,...
Springer, 2008, -296 p. Large collections of digital multimedia data are continuously created in different fields and in many application contexts. Application domains include web searching, cultural heritage, geographic information systems, biomedicine, surveillance systems, etc. The quantity, complexity, diversity and multi-modality of these data are all exponentially...
CRC Press, 2022. — 283 p. — ISBN .978-1-003-17008-2. Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning:...
Springer, 2018. - 161 p. - ISBN: 3030001288 This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in...
New York: Springer, 2020. — 195 p. This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning,...
Benjamin M. Marlin, 2008. A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy. Decision Theory, Inference, and Learning Optimal Prediction and Minimizing Expected Loss The Bayesian Framework The Maximum a Posteriori Framework The Direct Function Approximation Framework Empirical Evaluation Procedures A Theory of Missing Data Categories...
Gulf Professional Publishing, 2020. — 420 p. — ISBN: 978-0-12-817736-5. This book develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify...
2018. — 54 p. Are you looking for a foundational book to get you started with the basic concepts of Machine Learning? My book will explain you the basic concepts in ways that are easy to understand. Once you’ve read this book, you’ll have a solid grasp on the core principles that will make it easier to step to a more advanced book should you want to learn more.
Springer, 2001 — 324 p. — ISBN13: 978-3540424901 In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial...
New York: Autoedition, 2014. — 261 p. This course will focus on theoretical aspects of Statistical Learning and Sequential Prediction. Until recently, these two subjects have been treated separately within the learning community. The course will follow a unified approach to analyzing learning in both scenarios. To make this happen, we shall bring together ideas from probability...
Berkeley, CA: Apress, 2019. — 163 p. — ISBN: 978-1-4842-4297-1. Implement machine learning models in your iOS applications. This short work begins by reviewing the primary principals of machine learning and then moves on to discussing more advanced topics, such as CoreML, the framework used to enable machine learning tasks in Apple products. Many applications on iPhone use...
Leanpub, 2019. — 133 p. Deep Learning has revolutionized the Machine Learning field. Python tools like Scikit-Learn, Pandas, TensorFlow, and Keras allows you to develop applications powered by Machine Learning.
Springer, 2013. — 388 p. This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance...
Springer, 2019. — 418 p. — ISBN: 3030261522. The book introduces the latest methods and algorithms developed in machine and deep learning (hybrid symbolic-numeric computations, robust statistical techniques for clustering and eliminating data as well as convolutional neural networks ) dealing not only with images and the use of computers, but also their applications to...
Andriy Burkov, 2019. - 160 p. - ISBN: 199957950X. Final version ! Completed on 5.7.2019 The last twenty years have witnessed an explosion in the availability of enormous quantities of data and, correspondingly, of interest in statistical and machine learning applications. The impact has been profound. Ten years ago, when I was able to attract a full class of MBA students to my...
CRC Press, 2018. — 208 p. — ISBN: 1138593737. Behavior Trees (BTs) provide a way to structure the behavior of an artificial agent such as a robot or a non-player character in a computer game. Traditional design methods, such as finite state machines, are known to produce brittle behaviors when complexity increases, making it very hard to add features without breaking existing...
London, United Kingdom: Institution of Engineering and Technology, 2019. — 350 p. — ISBN: 978-1785616570, 1785616579. Machine learning explores the study and development of algorithms that can learn from and make predictions and decisions based on data. Applications of machine learning in wireless communications have been receiving a lot of attention, especially in the era of...
O’Reilly, 2016. — 79 p. — ISBN: 9781491956205. Machine learning is no longer just a tool for data scientists. By taking advantage of recent advances in this technology, UI and UX designers can find ways to better engage with and understand their users. This O’Reilly report not only introduces you to contemporary machine learning systems, but also provides a conceptual framework...
New York: Springer, 2019. — 262 p. This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML...
Wiley-IEEE, 2020. — 474 p. — ISBN: 9781119562252. Due to its powerful nonlinear mapping and distribution processing capability, deep NN-based machine learning technology is being considered as a very promising tool to attack the big challenge in wireless communications and networks imposed by the explosively increasing demands in terms of capacity, coverage, latency, efficiency...
Packt Publishing, 2021. — 737 p. — ASIN B08PDFXXRL. Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with...
Springer, 2013. — 506 p. This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the...
IGI Global, 2017 - 129 p. Software development and design is an intricate and complex process that requires a multitude of steps to ultimately create a quality product. One crucial aspect of this process is minimizing potential errors through software fault prediction. Enhancing Software Fault Prediction With Machine Learning: Emerging Research and Opportunities is an...
Morgan & Claypool, 2012. — 114 p. — ISBN: 978-1608457250 The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "Oracle" (e.g., a human annotator) that already...
Department of Electrical Engineering and Computer Sciences University of California at Berkeley, 2017. — 164 p. Linear Classifiers and Perceptrons Perceptron Learning; Maximum Margin Classifiers Soft-Margin Support Vector Machines; Features Machine Learning Abstractions and Numerical Optimization Decision Theory; Generative and Discriminative Models Gaussian Discriminant...
Manning Publications, 2025. — 373 p. — ISBN: 1633438759. Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems. From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside, you’ll find...
Springer, 2017. — 97 p. — (SpringerBriefs in Economics). — ISBN: 978-3-319-69013-1. This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing...
2nd Edition. — Packt Publishing, 2020. — 798 p. — ISBN: 978-1-83882-029-9. Code files only! Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in...
Jorge Brasil, 2023. — 314 p. Does the complexity of calculus in machine learning leave you feeling lost in a thicket of equations? Are you eager to find a guide that maps out this territory clearly and easily? Please enter a unique exploration where the world of calculus is demystified through the fascinating biology of bees, offering a perspective on mathematics that is as...
Packt Publishing, 2018. — 500 p. — ISBN: 1509304444. !Only code files Unleash Google's Cloud Platform to build, train and optimize machine learning models Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine...
Leanpub, 2022-08-22. — 155 p. Learn how to implement various feature selection methods in a few lines of code and train faster, simpler, and more reliable Machine Learning models. Using Python open-source libraries, you will learn how to find the most predictive features from your data through the filter, wrapper, embedded, and additional feature selection methods. Feature...
Academic Press, 2020. — 394 p. — ISBN: 978-0-12-815739-8. This book is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for...
Stanford University, 1998. — 179 p. My intention is to pursue a middle ground between a theoretical textbook and one that focusses on applications. The book concentrates on the important ideas in machine learning. I do not give proofs of many of the theorems that I state, but I do give plausibility arguments and citations to formal proofs. And, I do not treat many matters that...
Wiley, 2012. — 416 p. — (Wiley Series in Probability and Statistics). — ISBN: 0470596694, 9780470596692. Learn the science of collecting information to make effective decisionsEveryday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information...
Springer, 2006, -482 p. Third InternationalWorkshop, MLMI 2006, Bethesda, MD, USA, May 1-4, 2006. Revised Selected Papers. The workshop was organized and sponsored jointly by the US National Institute for Standards and Technology (NIST), three projects supported by the European Commission (Information Society Technologies priority of the sixth Framework Programme)—the AMI and...
Cham: Springer International Publishing, 2018. — 353 p. — ISBN: 978-3-319-99492-5. This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing the machine learning theory. The 12 revised full papers and 4 short papers included in this volume were presented at the conference "Braverman Readings in Machine Learning:...
Manning Publications, 2024. — 328 p. — ISBN-13: 978-1633439214. Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in-Depth you’ll explore practical...
Cham: Springer I, 2017. — 336 p. — ISBN: 978-3-319-47194-5. The topic of this monograph falls within the, so-called, biologically motivated computing paradigm, in which biology provides the source of models and inspiration towards the development of computational intelligence and machine learning systems. Specifically, artificial immune systems are presented as a valid metaphor...
Dordrecht: Springer Science+Business Media B.V, 2011. — 607 p. — ISBN: 978-90-481-9419-3. A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering...
Packt Publishing, 2016. — 50 p. It is a well-established fact that we, as human beings, learn through experience. During our early childhood, we learn to imitate sounds, form words, group them into phrases, and finally how to talk to another person. Later, in elementary school, we are taught numbers and letters, how to recognize them, and how to use them to make calculations...
O’Reilly Media, 2024 - 307 p. - ISBN: 1098146549. As tech products become more prevalent today, the demand for machine learning professionals continues to grow. However, the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu...
O’Reilly Media, 2011. — 148 p. — ISBN: 9781449314309. You’ll learn how to write algorithms that automatically sort and redirect email based on statistical patterns. Authors Drew Conway and John Myles White approach the process in a practical fashion, using a case-study driven approach rather than a traditional math-heavy presentation. This book also includes a short tutorial on...
Leanpub, 2023. — 192 p. This book introduces you to the 42 most commonly used machine learning algorithms in an understandable way. Machine Learning (ML) refers to the development of AI systems that can perform tasks due to a “learning process” based on data. This is in contrast to approaches and methods in symbolic AI and traditional software development, which are based on...
Addison-Wesley Professional, 2019. — 588 p. — (Addison-Wesley Data & Analytics Series). — ISBN13: 978-0-13-484562-3. A complete beginner's guide to learning and building machine learning systems using Python. Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're a...
2nd ed. — O’Reilly Media, 2023. — 453 p. — ISBN: 1098134184. Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to create impressive generative deep learning models from scratch using Tensorflow and Keras, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers,...
New York: Springer, 2019. — 372 p. This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn...
Springer, 2020. — 157 p. — (Computer Architecture and Design Methodologies). — ISBN: 978-981-13-3322-4. This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware...
O’Reilly Media, 2017. — 67 p. — ISBN: 978-1-491-92406-8. By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment. You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for...
Medium, 2017. — 97 p. This guide is intended to be accessible to anyone . Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn’t necessary to have prior knowledge of them to gain value from this series. If you're more interested in figuring out which courses to take, textbooks to read, projects to attempt, etc. Take...
2nd edition. — 2015. — 263 p. This is a set of lecture notes for an undergraduate class on metaheuristics. The first version of the notes was written for a course I taught in Spring of 2009. As these are lecture notes for an undergraduate class on the topic, which is unusual, these notes have certain traits. First, they’re informal and contain a number of my own personal biases...
Leanpub, 2022. — 111 p. The underlying goal of "Machine Learning Simplified" is to develop strong intuition for ML inside you. We would use simple intuitive examples to explain complex concepts, algorithms, or methods, as well as democratize all mathematics behind Machine Learning (ML). After reading this book, you would understand everything that comes into the scope of...
Springer, 2021. — 460 p. — ISBN: 9811519668. Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field even further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced...
Springer, 2023. — 457 p. — ISBN: 3031333411. This book is meant as a textbook for undergraduate and graduate students who are willing to understand essential elements of Machine Learning from both a theoretical and a practical perspective. The choice of the topics in the book is made based on one criterion: whether the practical utility of a certain method justifies its...
Cham: Springer International Publishing, 2018. — 118 p. — ISBN: 978-3-319-71489-9. This book explores break-through approaches to tackling and mitigating the well-known problems of compiler optimization using design space exploration and machine learning techniques. It demonstrates that not all the optimization passes are suitable for use within an optimization sequence and...
Apress, 20198. — 354 p. — ISBN: 978-1484251201. Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine...
Butterworth-Heinemann, 2018. — 364 p. — ISBN: 978-0-12-812976-0. This book presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those...
Springer, 2020. — 573 p. — ISBN: 3030410676. This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of...
Independently published, 2024. — 238 p. ISBN-13 979-8332452369. This book delves into the intricate relationship between mathematics and Machine Learning, providing readers with a comprehensive understanding of the mathematical concepts that underpin modern AI. From linear algebra and calculus to probability theory and statistics, each chapter explores a different mathematical...
Oxford: Claredon Press, 1994. - 488 p. The present volume records the Machine Intelligence Workshop of 1992, held at Strathclyde University's Ross Priory retreat on Loch Lomond, Scotland. Here the series entered not only its second quarter-century but a new phase. As can be seen in these pages, machine learning emerged to declare itself as a seed-bed of new theory, as a...
World Scientific Publishing Company, 2020. — 550 p. — ISBN: 978-9811207716. This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory...
The MIT Press, 2016, 801 p., ISBN-13 978-0262035613 An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” Elon Musk, cochair of OpenAI; cofounder and...
Springer, 2020. — 290 p. This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender systems, malware detection, intrusion detection and sentiment analysis. It explains how such classification problems can be solved using various statistical and machine learning methods, including K nearest neighbours,...
O’Reilly Media, Inc., 2022. — 331 p. — ISBN: 978-1-492-08525-6. Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing...
Morgan Kaufmann, 1990. — 815. As the field of machine learning enjoys unprecedented growth and attracts many new researchers, there is a need for regular summaries and comprehensive reviews of its progress. This volume is a sequel to the previous volumes of same title: Volume I appeared in 1983, Volume II in 1986. Volume III presents sample of machine learning research...
Syncfusion, 2017. — 114 p. Support Vector Machines (SVMs) are some of the most performant off-the-shelf, supervised machine-learning algorithms. In Support Vector Machines Succinctly, author Alexandre Kowalczyk guides readers through the building blocks of SVMs, from basic concepts to crucial problem-solving algorithms. He also includes numerous code examples and a lengthy...
O’Reilly, 2020. — 408 p. — ISBN: 9781098115784. 2020-10-15: First Release The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of...
Wiley, 2021. — 216 p. — ISBN: 978111968236. Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series...
River Publishers, 2025. — 172 p. — ISBN978-87-7004-653-4. This is an essential resource for beginners and experienced practitioners in Machine Learning. This comprehensive guide covers a broad spectrum of machine-learning topics, starting with an in-depth exploration of popular machine-learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow,...
O’Reilly Media, Inc., 2023. — 279 p. — ISBN: 978-1-098-11913-3. Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit...
The MIT Press, 2022. - 855 p. - ISBN: 0262046822. Book draft from 9.5.2022 (the book is officially published in March 2022!) A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning)...
Springer Berlin Heidelberg, 2011. — 262 p. — ISBN: 978-3-642-22910-7. This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010,...
BISAC: Computers / Intelligence (AI) & Semantics, 2018. — 394 p. I challenge you to find a field as interesting and exciting as Deep Learning. This book is a spin-off from my previous book "The Deep Learning AI Playbook". The Playbook was meant for a professional audience. This is targeted to a much wider audience. There are two kinds of audiences, those looking to explore and...
Self-published, 2021 — 675 p. — ISBN: 978-1-6641-5127-7. This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest...
Independently published, 2023. — 258 p. "Mathematics for Machine Learning: A Deep Dive into Algorithms" is a comprehensive guide that bridges the gap between mathematical theory and practical applications in the dynamic world of Machine Learning. Whether you're a data science enthusiast, a budding Machine Learning engineer, or a seasoned practitioner, this book equips you with...
Sanshodhana, 3016. - 39 p. - ISBN: 1520269277 This book is an introduction to theory and experiments in Machine Learning. The book has simple examples to explain what is possible using Machine Learning, this first edition has cut down on the mathematical aspect and concentrates on logical connections in explaining Machine Learning through experiments.
Academic Press, 2025. — 1220 p. Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modeling methods,...
Qiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan. — Cambridge University Press, 2020. — 393 p. — ISBN: 978-1-107-01690-3. Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small...
Springer, 2024. — 529 p. — ISBN: 3031532813. This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories: The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area...
Elsevier, 2015. — 84 p. Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate...
O’Reilly Media, 2016. — 82 p. — ISBN: 978-1-491-96297-8. The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding...
Springer, 2005, -377 p. First International Workshop, MLMI 2004, Martigny, Switzerland, June 21-23, 2004. Revised Selected Papers. The conference program featured invited talks, full papers (subject to careful peer review, by at least three reviewers), and posters (accepted on the basis of abstracts) covering a wide range of areas related to machine learning applied to...
O’Reilly Media, 2022. — 334 p. Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can create artificial data using simulations to train traditional machine learning...
2nd Edition. — O’Reilly Media, Inc., 2024. — 556 p. — ISBN: 978-1-098-15601-5. Using Machine Learning for products, services, and critical business processes is quite different from using ML in an academic or research setting — especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and...
2nd Edition. — O’Reilly, 2023. — 413 p. — ISBN: 978-1-098-13572-0. This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training...
Springer, 2012. — 602 p. — ISBN10: 3642353258, ISBN13: 978-3642353253. This book constitutes the refereed proceedings of the Second International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2014, held in Cairo, Egypt, in November 2014. The 49 full papers presented were carefully reviewed and selected from 101 initial submissions. The papers are...
New York: Springer, 2017. — 231 p. This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the...
Springer, 2024. - 464 p. - ISBN: 3031469895. Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory...
Packt Publishing, 2019. — 448 p. — ISBN: 978-1-78934-634-3. Optimize your marketing strategies through analytics and machine learning Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and...
Springer, 2011. — 370 p. Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition,...
Springer, 2020. — 180 p. — ISBN: 978-3-030-35970-6. This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy-latency-accuracy trade-offs. From first principles to lab prototypes, this book provides a comprehensive view of this emerging topic for both the practicing engineer in industry and the...
O’Reilly Media, 2023. - 265 p. - ISBN: 1492097675. There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual...
MIT Press, 1990. — 176 p. The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning. Theorems are...
Wiley-Scrivener, 2025. — 403 p. — ISBN: 978-1-394-27255-6. “Deep Reinforcement Learning and its Industrial Use Cases: Harnessing AI for Real-World Applications” is an essential guide that supplies complex theories, practical insights, and diverse case studies behind deep reinforcement learning. This book offers a comprehensive look into how DLR is revolutionizing fields by...
Leanpub, 2023. — 216 p. Master Machine Learning interpretability with this comprehensive guide to SHAP – your tool for communicating model insights and building trust in all your Machine Learning applications. Machine Learning is transforming fields from healthcare diagnostics to climate change predictions through their predictive performance. However, these complex Machine...
2nd Edition. — Leanpub, 2022-03-04. — 329 p. This book teaches you how to make Machine Learning models more interpretable. Machine learning has great potential for improving products, processes, and research. But computers usually do not explain their predictions which is a barrier to the adoption of Machine Learning. This book is about making Machine Learning models and their...
Springer, 2008, -318 p. 4th International Workshop, MLMI 2007, Brno, Czech Republic, June 28-30, 2007. Revised Selected Papers. This book contains a selection of revised papers from the 4th Workshop on Machine Learning for Multimodal Interaction (MLMI 2007), which took place in Brno, Czech Republic, during June 28–30, 2007. As in the previous editions of the MLMI series, the 26...
Springer, 2006, -502 p. Second InternationalWorkshop, MLMI 2005, Edinburgh, UK, July 11-13, 2005. Revised Selected Papers. The conference program featured invited talks, full papers (subject to careful peer review, by at least three reviewers), and posters (accepted on the basis of abstracts) covering a wide range of areas related to machine learning applied to multimodal...
Wiley, 2023. — 701 p. — ISBN: 978-1119828792. Machine Learning — also known as data mining or data analytics — is a fundamental part of Data Science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in RapidMiner provides a comprehensive...
O’Reilly Media, Inc., 2024. — 359 p. — ISBN: 978-1-098-15161-4. What is a data platform? Why do you need it? What does building a data and Machine Learning (ML) platform involve? Why should you build your data platform on the cloud? This book starts by answering these common questions that arise when dealing with data and ML projects. We then lay out the strategic journey that...
Reactive Publ., 2023. — 663 p. Unlock the full potential of machine learning with "Machine Learning," your essential guide to conquering the cutting-edge tools that are reshaping the landscape of Data Science and Artificial Intelligence (AI). Whether you're a student, a Data Science professional, or simply a tech enthusiast eager to delve into the world of Machine Learning,...
Wiley, 2024. — 510 p. — ISBN: 9781394220632. Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics,...
Panda Ghana Publishing, 2019. — 246 p. Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics - K Nearest Neighbours; K Means Clustering; Naïve Bayes Classifier; Regression Methods; Support Vector Machines; Self-Organizing Maps; Decision Trees; Neural Networks; Reinforcement Learning General Matters K Nearest...
Independently Published, 2019. — 151 p. — ASIN B082MPL2Z2. One of the most widely recognized AI methods utilized for handling huge information is AI, a self-versatile calculation that shows signs of improvement examination and examples with experience or with recently included information. In the event that a computerized installment organization needed to identify the event or...
Boston, MA : Springer-Verlag US, 2009. — 367 p. — ISBN: 978-0-387-88735-7. Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems turns out to be a fertile ground where many tasks can be formulated as learning problems...
Springer, 2012. — 331 p. It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing...
AI Publishing, 2020. — 308 p. — ISBN B08QJMNVCX. 10 Machine Learning Projects Explained from Scratch Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to understand potential risks better. The ever-expanding data, cost-effective data storage, and competitively priced powerful...
Wiley, 2023. — 592 p. — eBook ISBN: 978-1-394-20910-1. In-depth resource covering machine and deep learning methods using MatLAB tools and algorithms, providing insights and algorithmic decision-making processes Machine and Deep Learning Using MatLAB introduces early career professionals to the power of MatLAB to explore machine and deep learning applications by explaining the...
4th ed. — The MIT Press, 2020. — 712 p. — ISBN: 9780262358064. A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks. The goal of machine learning is to program computers to use example data or experience to solve a given problem. Machine learning underlies such exciting new...
2nd ed. — Singapore: World Scientific Publishing Company, 2022. — 1055 p. — ISBN: 9811245142. The Handbook on Computer Learning and Intelligence is a second edition that aims to be a one-stop-shop for the various aspects of the broad research area of computer learning and intelligence. This field of research evolved so much in the last five years that it necessitates this new...
CRC Press, 2023. — 310 p. This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application. This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on...
Princeton: LN, 2023. — 256 p. Supervised Learning. Linear Regression: An Introduction. A Warm-up Example. Using Linear Regression for Sentiment Prediction. Importance of Featurization. Linear Regression in Programming. Statistical Learning: What It Means to Learn. A Warm-up Example. Summary of Statistical Learning. Implications for Applications of Machine Learning. Optimization...
Springer, 2023. — 763 p. — (Genetic and Evolutionary Computation). — ISBN: 9819938139. This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part...
Machine Learning Mastery, 2020. — 398 p. Data preparation involves transforming raw data in to a form that can be modeled using machine learning algorithms. Cut through the equations, Greek letters, and confusion, and discover the specialized data preparation techniques that you need to know to get the most out of your data on your next project. Using clear explanations,...
Independently published, 2020. — 294 p. — ISBN B085PSKBRH. 3 Books in 1! Are you searching for the fastest way to discover the secrets of the fascinating world of Computer Science? For a very limited time, you have the opportunity to get three best-selling guides in a single phenomenal mega bundle: if you are a student or a professional looking for more technical skills, then...
Amazon.com Services LLC., 2020. — 140 p. Are you looking for a guide that will teach you all you need to know about machine learning? Are you looking for a way to learn how to write algorithms from scratch? Then read on… Artificial intelligence is a common part of our lives, and we use it daily. Machine learning is one application of artificial intelligence and is where...
Leanpub, 2018. — 147 p. Do you want to start using R for crunching machine learning models right from the start with examples? The book is about quickly entering the world of creating machine learning models in R . The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, features engineering and association rules. The...
2nd Edition. — Manning Publications, 2022. — 568 p. Deep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at...
Wiley-IEEE Press, 2025. — 445 p. — ISBN: 978-1394272945. Enables readers to develop foundational and advanced vectorization skills for scalable Data Science and Machine Learning and address real-world problems. Offering insights across various domains such as Computer Vision and natural language processing (NLP), Vectorization covers the fundamental topics of vectorization...
Dargslan Publishing, 2024. — 607 p. "Machine Learning with Python: A Comprehensive Guide" is your gateway to mastering the exciting world of Machine Learning (ML) using Python. This book is meticulously crafted to cater to beginners, professionals, and students alike, offering a step-by-step approach to understanding and implementing ML concepts. Key Features: Accessible...
KJ Publishing, 2022. — 166 p. Machine Learning is a complex subject area. Our goal in this lesson is to introduce you to some of the most common terms and ideas used in Machine Learning. I will then walk you through the different steps involved in Machine Learning (ML) and finish with a series of examples that use Machine Learning to solve real-world situations. Machine...
Springer, 2022. — 585 p. Electronic design automation (EDA) is a software technology that attempts to let computers undertake chip design tasks so that we can handle complexities beyond manual design capabilities. Although conventional EDA techniques have led to colossal design productivity improvement, they face the fundamental limit that most EDA problems are NP-hard. They,...
Springer, 2018. — 726 p. This book presents the refereed proceedings of the third International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2018, held in Cairo, Egypt, on February 22–24, 2018, and organized by the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things,...
Springer/Posts & Telecom Press, 2023. — 398 p. Firstly, it takes common competition scenarios as a guide by giving the main processes of using Machine Learning to solve real-world problems, namely problem modeling, data exploration, feature engineering, and model training. And then lists the main points of difficulties, general ideas with solutions in the whole process....
2nd ed. — School of Management University of Toronto, 2020. — 274 p. — ISBN: 9798644074372. This book is for business executives and students who want to learn about the tools used in machine learning. In creating the second edition, John Hull has continued to improve his material and added three new chapters. The book explains the most popular algorithms clearly and succinctly...
Springer, 2012. — 287 p. This monograph presents key method to successfully manage the growing complexity of systems where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limits and new ways are nowadays required. The transition from adaptable to evolvable and finally to self-evolvable systems is...
Packt Publishing, 2020. — 1470 p. Machine Learning for Trading. Market and Fundamental Data. Alternative Data for Finance. Alpha Factor Research. Strategy Evaluation. The Machine Learning Process. Linear Models. Time Series Models. Bayesian Machine Learning. Decision Trees and Random Forests. Gradient Boosting Machines. Unsupervised Learning. Working with Text Data. Topic...
CRC Press, 2022. — 298 p. — ISBN: 0367433540. Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and...
Tel-Aviv: Slomo Keshani, 2020. - 410 p. - ISBN 1916243568. Deep Learning Interviews is home to hundreds of fully-solved problems , from a wide range of key topics in AI . It is designed to both rehearse interview or exam specific topics and provide machine learning M.Sc./Ph.D. students, and those awaiting an interview a well-organized overview of the field . The problems it...
No Starch Press, 2021. — 291 p. — ISBN: 978-1-7185-0056-3. A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial...
Manning Publications, 2023. — 362 p. — (Final Release). — ISBN: 9781617299520. Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. In Evolutionary Deep Learning you will learn how:...
Boca Raton: CRC Press, 2025. — 415 p. Mathematical Engineering of Deep Learning provides a complete and concise overview of Deep Learning using the language of mathematics. The book provides a self-contained background on Machine Learning and optimization algorithms and progresses through the key ideas of Deep Learning. These ideas and architectures include deep neural...
Singapore: World Scientific Publishing Company, 2023. - 692 p. - ISBN: 9811254176. Machine Learning (ML) has become a very important area of research widely used in various industries. This compendium introduces the basic concepts, fundamental theories, essential computational techniques, codes, and applications related to ML models. With a strong foundation, one can...
Petaluma US : Roundtree Press, 2018. — 104 p. Artificial Intelligence Studio at Globant. Many industries are leveraging artificial intelligence (AI) to stay ahead of the curve. As cognitive and AI platforms become smarter, companies are using deep neural networks to give them abilities they didn’t have before. It’s the augmented intelligence revolution, with AI enhancing...
Wiley, 2021. - 467 p. - ISBN: 9781119724018 2nd.ed. Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android — as in the movie Ex Machina — it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems...
Manning Publications, 2022. — 358 p. — ISBN: 978-1617299797. Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This instantly...
Code Planet, 2025. — 231 p. Machine Learning with Python in this all-in-one guide designed for beginners and experienced developers alike! Whether you're diving into supervised and unsupervised learning, exploring neural networks, or mastering real-world applications, this book provides step-by-step explanations, hands-on examples, and expert insights. Machine Learning (ML) is...
3rd ed. — Springer, 2023. — 974 p. — ISBN: 3031246276. This book is a major update to the very successful first and second editions (2005 and 2010) of the Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications, and aspects are...
Independently published, 2023. — 176 p. In the past decade, Artificial Intelligence has been making waves. From self-driving cars to Siri to Alexa, Artificial Intelligence (AI) is everywhere. But what exactly is it? The term "artificial intelligence" was coined in 1956 by John McCarthy, a computer scientist at Dartmouth College. His work was based on the idea that computers...
Cambridge University Press, 2023. — 412 p. — ISBN: 978-1-009-12330-3. Parked with real-world examples, industry insights, and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are...
Singapore: World Scientific Publishing Company, 2021. - 881 p. - ISBN: 9811241198. This unique compendium gives an updated presentation of clustering, one of the most challenging tasks in machine learning . Clustering is a part of machine learning that seeks to identify groups into sets of objects such that objects that belong to the same group are as similar as possible , and...
StatQuest, 2022. — 305 p. — ISBN: 979-8811583607. Machine Learning is awesome and powerful, but it can also appear incredibly complicated. That’s where The StatQuest Illustrated Guide to Machine Learning comes in. This book takes the machine learning algorithms, no matter how complicated, and breaks them down into small, bite-sized pieces that are easy to understand. Each...
Springer International Publishing Switzerland. 2014. - 129 p. Indisputably, Support Vector Machines (SVM) and Evolutionary Algorithms (EA) are both established algorithmic techniques and both have their merits and success stories. It appears natural to combine the two, especially in the context of classification. Indeed, many researchers have attempted to bring them together in...
Academic Press, 2020. - 534 p. - ISBN: 9780128213797 Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems...
Manning Publications, 2024. — 248 p. — ISBN: 978-1617299025. Practical patterns for scaling Machine Learning from your laptop to a distributed cluster. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects. Construct machine learning pipelines with data ingestion, distributed...
New York: Springer, 2019. — 548 p. This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary;...
Birmingham: Packt Publishing, 2023. — 423 p. — ISBN: 1837634084. Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E, and GPT-3. Key Features Learn to diagnose the need for regularization in any machine learning model. Regularize different ML models using a variety of techniques...
De Gruyter, 2024. — 210 p. — ISBN: 978-3-11-128899-4. This book is an introduction to Machine Learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known Supervised Machine Learning algorithms, detailing the existing theory...
Berlin: Springer, 2005. - 434 p. This volume is composed of 20 chapters selected from the recent myriad of novel SVM applications, powerful SVM algorithms, as well as enlightening theoretical analysis. Written by experts in their respective fields, the first 12 chapters concentrate on SVM theory, whereas the subsequent 8 chapters emphasize practical applications, although the...
2nd Edition. — Cambridge University Press, 2020. — 594 p. — ISBN: 978-1-108-48072-7. With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all...
Springer, 2023. — 235 p. — ISBN: 978-3-031-18551-9. The Novel Financial Applications of Machine Learning and Deep Learning: Algorithms, Product Modeling, and Applications presents the state-of-the-art application of Machine Learning (ML) and Deep Learning (DL) in the domain of finance. We will present a combination of empirical evidence to diverse fields of finance so that this...
Abhishek Thakur, 2020. - 300 p. - ISBN: 8269211508 This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you...
AppCoda, 2020. — 330 p. — ISBN: 978-9-887535-00-3. Machine learning, now more than ever, plays a pivotal role in almost everything we do in our digital lives. Whether it’s interacting with a virtual assistant like Siri or typing out a message to a friend, machine learning is the technology facilitating those actions. It’s clear that machine learning is here to stay, and as...
AI Publishing LLC, 2020. — 301 p. — ISBN B08LSLHBR8. Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that’s right. Based on a significant amount of data and evidence, it’s obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are driving business results. Whether it’s healthcare, e-commerce,...
Akshay B R, Sini Raj Pulari, T S Murugesh, Shriram K Vasudevan. — CRC Press, 2025. — 259 p. — ISBN: 978-1-032-67666-1. Machine Learning is an exciting and rapidly growing field that is all about creating algorithms and models that enable computers to recognize patterns, make predictions, and improve their performance over time. It empowers computers to learn from data and...
The MIT Press, 2024. - 394 p. - ISBN: 0262049376. The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches. Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a...
IGI Global, 2020. — 328 p. — ISBN: 978-1799803010. In todays developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the...
Springer, 2021. — 182 p. — (SpringerBriefs in Petroleum Geoscience & Engineering). — ISBN: 978-3-030-71767-4. This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used...
Apress, 2019. — 248 p. — ISBN13: 978-1-4842-5106-5. Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective...
Independently published, 2021. — 225 p. — ASIN B09BNYDH5F. Have you always been curious about machine learning but do not know where to start. Or perhaps your new job requires you to learn machine learning but you are overwhelmed with all the information online. What is machine learning? What is Scikit-Learn? What does the fit() method that you see on so many online tutorials...
CRC, 2020. — 430 p. — ISBN: 9781466510845. Based on the author’s experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the...
Academic Press, 2023. — 326 p. — eBook ISBN: 9780128229057. Machine Learning for Biomedical Applications: Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple...
CRC Press, 2024. — 344 p. — eBook ISBN: 978-1-003-24019-8. Providing an essential and unique bridge between the theories of signal processing, machine learning, and artificial intelligence (AI) in music, this book provides a holistic overview of foundational ideas in music, from the physical and mathematical properties of sound to symbolic representations. Combining signals and...
Microsoft Press, 2020. — 392 p. — ISBN: 978-0-13-556566-7. Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic...
CRC Press, 2023. — 478 p. — ISBN: 978-1-032-26829-3. Machine Learning: Concepts, Techniques, and Applications starts at the basic conceptual level of explaining Machine Learning and goes on to explain the basis of Machine Learning algorithms. The mathematical foundations required are outlined along with their associations with Machine Learning. The book then goes on to describe...
Boca Raton: CRC Press, 2024. — 671 p. This book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. In recent decades, machines have played a significant role in making human life more comfortable. Machine...
Springer, 2008. — 173 p. The book presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more...
Academic Press, 2021. — 661 p. — ISBN: 9780323901994. Principles and Labs for Deep Learning provide the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of...
Packt Publishing, 2019. — 448 p. — ISBN: 978-1-78934-634-3. Code files only! Optimize your marketing strategies through analytics and machine learning Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the...
New York: Springer, 2007. — 738 p. This book represents a comprehensive introduction into both conceptual and rigorous brain and cognition modeling. It is devoted to understanding, prediction and control of the fundamental mechanisms of brain functioning. The reader will be provided with a scientific tool enabling him or her to perform a competitive research in brain and...
Cambridge University Press, 2022. — 420 p. — ISBN10: 1108837042; ISBN13: 978-1108837040. This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability, and statistics but has no prior exposure to machine learning. Coverage...
CRC Press, 2025. — 300 p. — ISBN: 978-1-032-55232-3. This book presents the research into and application of Machine Learning in quantum computation, known as Quantum Machine Learning (QML). It presents a comparison of Quantum Machine Learning, classical Machine Learning, and traditional programming, along with the usage of quantum computing, toward improving traditional...
2nd edition. — Boston: The MIT Press, 2020. — 853 p. The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including...
2nd ed. — The MIT Press, 2020. — 853 p. — ISBN: 9780262044691. The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications...
Manning Publications, 2024. — 667 p. — ISBN: 163343883X. Solve design, planning, and control problems using modern machine learning and AI techniques. In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn: Machine learning methods for search and optimization problems. The core concepts of search and optimization. Deterministic and...
Packt Publishing, 2020. — 530 p. Build, train, and deploy end-to-end machine learning and deep learning pipelines C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with...
Independently published, 2023. — 520 p. — ASIN: B0CP11GTC1 (converted to PDF). "Machine Learning in Python for Everyone" is your comprehensive guide to mastering machine learning with the Python programming language. Whether you're a novice looking to embark on your data science journey or an experienced practitioner aiming to refine your skills, this book provides a structured...
Springer, 2023. - 529 p. - ISBN: 981993916X. This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy...
Cambridge: Cambridge University Press, 2022. - 351 p. - ISBN: 1108843603. This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework, it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic...
Boca Raton: CRC Press, 2023. — 464 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — ISBN: 1032462140. The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however, ML has also been described as a black box and very hard to understand. Machine Learning, Animated eases you into basic ML concepts and summarizes the learning process in...
Palm Bay: CRC Press/Apple Academic Press, 2022. — 685 p. This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather...
Independently published, 2023. — 245 p. In the ever-changing world of finance and trading, the search for a competitive edge has been a constant driver of innovation. Over the last few decades, the field of quantitative trading has emerged as a powerful force, pushing the boundaries of what is possible and reshaping the way we approach the market. At the heart of this...
IGI Global, 2024. — 473 p. — ISBN: 9781668485330. Machine Learning can solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assist researchers in learning and implementing these...
Springer Cham, 2024. — 249 p. — eBook ISBN: 978-3-031-56431-4. Clearly explains the mathematical underpinnings essential for a robust understanding of machine learning algorithms. Coverage is tailored to students and researchers in all natural science areas, in addition to computer scientists. Accessible yet rigorous mathematical approach supplemented with website and...
New York: lulu.com, 2021. — 368 p. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like...
Wiley, 2023. — 610 p. An accessible and practical framework for machine learning applications and solutions for civil and environmental engineers. This textbook introduces engineers and engineering students to the applications of Artificial Intelligence (AI), Machine Learning (ML), and machine intelligence (MI) about civil and environmental engineering projects and problems,...
Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. — 124 p. — ISBN: 978-3-662-48838-6. The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical...
Leanpub, 2023. — 68 p. Reinforcement Learning: Unleashing the AI Agent's Power" Discover the thrilling world of Reinforcement Learning (RL) in our engaging eBook! Learn the fundamentals of RL, from AI agents and environments to rewards and actions. Explore real-world applications like robotics, healthcare, and personalized recommendations, where RL is transforming industries....
Apress Media LLC, 2022 — 347 p. — ISBN-13: 978-1-4842-7920-5. Here is the perfect comprehensive guide for readers with basic to intermediate-level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep...
Cham: Springer International Publishing, 2019. — 107 p. — ISBN: 978-3-319-98675-3. This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations,...
Packt Publishing, 2024. — 603 p. — ISBN.978-1-80512-250-0. Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS Purchase of the print or Kindle book includes a free PDF eBook. Key Features. Solve large-scale ML challenges in the cloud with several open-source and AWS tools and frameworks. Apply risk...
Independently published, 2021. — 352 p. Hands-on and written in jupyter notebook...feel the heat! Data Science from Scratch (Part 2) This part is entirely focus on business machine learning. The book covers theory and complete pipeline along with hands-on exercise for widely used machine learning algorithms.
Apple Academic Press, 2022. — 264 p. — ISBN: 978-1-003-05512-9. This new volume, Empowering Artificial intelligence Through Machine Learning: New Advances and Applications, discusses various new applications of machine learning, a subset of the field of artificial intelligence. Artificial intelligence is considered to be the next-big-game changer in research and technology, The...
Leanpub, Sebastian Raschka, 2023. — 231 p. Have you recently completed a Machine Learning or Deep Learning course and wondered what to learn next? With 30 questions and answers on key concepts in Machine Learning and AI, this book provides bite-sized bits of knowledge for your journey to becoming a Machine Learning expert. Expand Your Machine Learning Knowledge Machine Learning...
3rd edition. — Springer, 2023. — 975 p. — ISBN: 978-3-031-24627-2. This book is a major update to the very successful first and second editions (2005 and 2010) of the Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications, and...
Apress Media, LLC, 2025. — 346 p. — (Maker Innovations Series). — ISBN-13: 979-8-8688-1293-4. Be a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform. You’ll start by exploring Machine learning in the context of...
Manning Publications Co., 2019. — 280 p. — ISBN: 978-1617293702. Code files only! MEAP Version 7 It’s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using...
Cambridge: Cambridge University Press, 2022. - 601 p. - ISBN: 1316512827. This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between...
BPB Publications, 2021 — 341 p. — ISBN: 9388511972. A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem. Key Features Develop a Conceptual and Mathematical understanding of Statistics. Get an overview of Statistical Applications in Python. Learn how to perform Hypothesis testing in Statistics. Understand why Statistics is...
IGI Global, 2024. — 327 p. — (Advances in Computational Intelligence and Robotics (ACIR) Book Series). — ISBN 979-8-3693-5271-7. In today's rapidly evolving world, the exponential growth of data poses a significant challenge. As data volumes increase, traditional methods of analysis and decision-making become inadequate. This surge in data complexity calls for innovative...
2nd edition. — CRC Press, 2023. — 498 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — eBook ISBN: 978-1-003-26487-3. Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic...
1991 — 90 p. The game of go is an ideal problem domain for exploring machine learning: it is easy to define and there are many human experts, yet existing programs have failed to emulate their level of play to date. Existing literature on go playing programs and applications of machine learning to games are surveyed. An error function based on a database of master games is...
O’Reilly Media, 2023. - 325 p. - ISBN: 1098146824. Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts...
Packt Publishing, 2024. — 398 p. Key Features: Generate labels for regression in scenarios with limited training data. Apply generative AI and large language models (LLMs) to explore and label text data. Leverage Python libraries for image, video, and audio data analysis and data labeling. Book Description: Data labeling is the invisible hand that guides the power of artificial...
Boca Raton: CRC Press, 2022. — 477 p. — ISBN13: 9781003258803. This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for...
New York: Information Science Reference, 2018. — 217 p. Teachers use e-learning systems to develop course notes and web-based activities to communicate with learners on one side and monitor and classify their progress on the other. Learners use it for learning, communication, and collaboration. Adaptive e-learning systems often employ learner models, and the behavior of an...
O’Reilly Media, 2020. — 432 p. — ISBN: 978149207305-5. Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised,...
Independently published, 2020. — 302 p. — ISBN B08NZW37N6. 4 Books in 1! Master the best methods for PYTHON. Learn how to programming as a pro and get positive ROI in 7 days with data science and machine learning Are you looking for a super-fast computer programming course? Would you like to learn the Python Programming Language in 7 days? Do you want to increase your trading...
Tan Publishing, 2023. — 217 p. Machine Learning Simplified: A Comprehensive Guide to Supervised, Unsupervised, and Semi-Supervised Algorithms. In today's data-driven world, Machine Learning has become an essential tool for businesses and organizations of all sizes. This powerful technology enables computers to learn from data without being explicitly programmed, making it...
Oxford University Press, 2020. — 257 p. — ISBN: 978019256309-5. Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and...
CRC Press, 2025. — 264 p. This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences. In short, it is a gateway to...
Reactive Publishing, 2023. — 365 p. — ISBN: B0CQX39XGV. Push the boundaries of machine learning with Python and elevate your data analysis skills to new heights! 'Supervised Machine Learning with Python' is the essential guide for professionals who have mastered the basics and are ready to dive into the more complex and powerful aspects of machine learning. If you are...
Springer, 2023. — 333 p. Transfer learning is one of the most important technologies in the era of Artificial Intelligence and Deep Learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, several relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and...
ISLAND: Manning Publications, 2022. — 578 p. — ISBN: 9781617298714. Field-tested tips, tricks, and design patterns for building Machine Learning projects that are deployable, maintainable, and secure from concept to production. Machine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You’ll adopt...
Cambridge: Cambridge University Press, 2023. — 468 p. — ISBN: 1009098381. The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with...
Routledge, 2021. — 259 p. — (Routledge Advanced Texts in Economics and Finance). — ISBN: 978-0-367-48081-3. This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock...
ITexLi, 2023. — 132 p. — ISBN: 0850145147 9780850145144 0850145139 9780850145137 0850145155 9780850145151. With contributions from a diverse group of authors, this book offers a multifaceted perspective on machine learning and data mining. Whether you’re an experienced researcher or a newcomer, this collection is an essential resource for staying at the forefront of these...
Ojula Technology Innovations, 2923. — 94 p. Learning algorithms and data structures from this book will help you become a better programmer. Algorithms and data structures will make you think more logically. Furthermore, they can help you design better systems for storing and processing data. They also serve as a tool for optimization and problem-solving. As a result, the...
De Gruyter, 2024. — 486 p. This book explains how to use the programming language Python to develop Machine Learning and Deep Learning tasks. Machine Learning (ML) is a discipline within the field of Artificial Intelligence (AI) that concentrates on the creation of algorithms and models, allowing computer systems to acquire knowledge and make forecasts or choices without the...
Academic Press / Elsevier, 2023. — 348 p. — ISBN: 978-0-323-96098-4. Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI...
McGraw-Hill LLC, 2025. — 249 p. This practical guide shows, step-by-step, how to use Machine Learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of...
BPB Publications, 2020 — 262 p. — ISBN: 9389845424, 978-9389845426. Get familiar with various Supervised, Unsupervised, and Reinforcement learning algorithms This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature...
Cambridge: Cambridge University Press, 2025. — 1158 p. — ISBN: 1009170244. Machine learning has become a dominant problem-solving technique in the modern world, with applications ranging from search engines and social media to self-driving cars and artificial intelligence. This lucid textbook presents the theoretical foundations of machine learning algorithms and then...
Springer, 2020. — 366 p. — ISBN: 3030276554. Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. The book is intended to be concise but...
Springer, 2023 — 295 p. — (Applied Mathematical Sciences). The once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. This book provides an insider's view of how to combine two mature fields, scientific computing, and...
Independently published, 2022. — 457 p. — ISBN13: 979-8437574973. Many market professionals consider unsupervised learning the following frontier in artificial intelligence, one that might hold the secret to the holy grail in AI study, the so-called general expert system. Considering that the majority of the world’s data is unlabeled, traditional supervised knowing can not be...
Springer, 2023. — 415 p. — (Statistics and Computing). — ISBN: 3031413369. This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata. It provides a balance between theory and applications and fosters an understanding and awareness of the availability of machine-learning methods over...
Chapman & Hall, 2025. — 344 p. — eBook ISBN: 978-1-003-29633-1. This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this relationship between the...
CRC Press, 2025. — 189 p. Bayesian data analysis and modeling linked with Machine Learning (ML) offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization. Two types of correlation play central roles in geotechnical site characterization: cross-correlation among soil...
Bentham Science Publishers, 2023. — 139 p. — eISBN: 978-981-5124-42-2. Machine learning is a subfield of artificial intelligence, broadly defined as a machine's capability to imitate intelligent human behavior. Like humans, machines become capable of making intelligent decisions by learning from their past experiences. Machine learning is being employed in many applications,...
Quantum Technologies, 2024. — 614 p. Become a Machine Learning Hero and Master Data Science with Python. In a world driven by data, mastering Machine Learning is your key to unlocking new opportunities and solving complex problems. Whether you're a beginner or a professional looking to sharpen your skills, Machine Learning Hero: Master Data Science with Python Essentials is...
IGI Global, 2021. - 351 p. - ISBN: 9781799854937 Probabilistic modeling represents a subject arising in many branches of mathematics, economics, and computer science. Such modeling connects pure mathematics with applied sciences. Similarly, data analyzing and statistics are situated on the border between pure mathematics and applied sciences. Therefore, when probabilistic...
Orange Education Pvt Ltd, AVA, 2024. — 714 p. — ISBN: 978-81-97651-20-5. The only MLOps guide you'll ever need. Key Features Acquire a comprehensive understanding of the entire MLOps lifecycle, from model development to monitoring and governance. Gain expertise in building efficient MLOps pipelines with the help of practical guidance with real-world examples and case studies....
Cambridge University Press, 2020. — 287 p. When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to...
Springer Singapore, 2023. — 295 p. — (Algorithms for Intelligent Systems) — eBook ISBN: 978-981-99-6645-5. Provides a rich set of chapters on machine learning and metaheuristic optimization. Emphasizes optimization algorithms such as PSO, ant colony optimization, cuckoo search algorithm, etc. Includes real-world examples with attention to theoretical aspects for better...
Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad (Editor). — Wiley, 2020. — 535 p. — ISBN: 9781119654742. Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online...
Elsevier, 2023. — 420 p. Advanced Machine Learning Techniques include the theoretical foundations of modern Machine Learning, as well as advanced methods and frameworks used in modern Machine Learning. Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques presents both the art of designing good learning algorithms, as well as the science of analyzing an...
Wiley, 2022. — 53 p. In this book, we focus on the commercial applications of graph analysis and graph-enhanced machine learning (ML), which takes the form of graph data science. We also use Neo4j graph technology to illustrate a graph data science platform. You take a quick look at graph data science and its uses before covering the journey of graph data science adoption. You...
Springer, 2024. - 280 p. - ISBN: 3031394763. This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field. Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the 'black box' approach of machine learning by reducing...
Leanpub, 2022-08-24. — 52 p. Learn how to implement various feature selection methods in a few lines of code utilizing the open-source Python library Feature-engine. Feature-engine is an open-source Python library for feature engineering and feature selection. It uses Pandas and Scikit-learn under the hood to engineer and select feature subsets. Feature selection is selecting a...
Morgan Kaufmann/Elsevier, 2024. — 410 p. — ISBN-13: 978-0-443-21857-6. Synthetic Data and Generative AI cover the foundations of Machine Learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression...
Manning Publications, 2021. — 502 p., ISBN: 9781617296819 In Machine Learning Bookcamp you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You’ll then...
O’Reilly Media, 2023. — 466 p. — ISBN: 1098102436. The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true...
O’Reilly Media, Inc., 2020. — 366 p. — ISBN: 978-1-492-05319-4. Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques...
NY, Shelter Island: Manning Publications Co., 2021. — 482 p. Data Pipelines with Apache Airflow teaches you the ins-and-outs of the Directed Acyclic Graphs (DAGs) that power Airflow, and how to write your own DAGs to meet the needs of your projects. With complete coverage of both foundational and lesser-known features, when you’re done you’ll be set to start using Airflow for...
Orchid Publishing, 2023. — 431 p. Are you ready to dive into the fascinating world of Machine Learning and Artificial Intelligence? Do you want to understand the technology that powers everything from personalized recommendations to self-driving cars? If so, "Machine Learning With Python Programming: 2023 A Beginners Guide" is the book you've been waiting for. This...
Leanpub, 2020. — 505 p. — ISBN: NA. This version was published on 2020-10-09 Core ML is pretty easy to use — except when it doesn’t do what you want. The Core ML Survival Guide is packed with tips and tricks for solving the most common Core ML problems. Updated for iOS 14 and macOS 11. Important: I will not be updating this book to the new features introduced with iOS 15 and...
Toronto: John C. Hull, 2020. — 274 p. Unsupervised Learning Linear and Logistic Regression Decision Trees SVMs Neural Networks Reinforcement Learning Natural Language Processing Model Interpretability Applications in Finance Issues for Society Answers to End of Chapter Questions
Sonar Publishing, 2023. — 362 p. "Python for Machine Learning: From Fundamentals to Real-World Applications" is your comprehensive roadmap to mastering Machine Learning with Python. Whether you're a beginner looking to enter the exciting world of Data Science or an experienced programmer aiming to delve deeper into Machine Learning, this book provides you with the knowledge and...
O’Reilly Media, 2022. — 99 p. — ISBN: 9781098107956. Early release Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall...
CRC Press, 2023. — 425 p. — (Smart Technologies for Engineers and Scientists). — ISBN: 978-1-032-47929-3. The text focuses on mathematical modeling and applications of advanced techniques of Machine Learning, and Artificial Intelligence, including artificial neural networks, evolutionary computing, data mining, and fuzzy systems to solve performance and design issues more...
2nd Revised Edition. — Cambridge University Press, 2025. — 426 p. — ISBN-13: 978-1-316-51886-1. As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers...
Springer, 2021. - 411 p. - ISBN: 9783030658991 This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple...
HiTeX Press, 2024. — 542 p. "Fundamentals of Machine Learning: An Introduction to Neural Networks" is an accessible yet comprehensive guide designed for individuals new to the intriguing world of Machine Learning. This book meticulously unfolds the foundational principles and techniques in Machine Learning while placing a keen emphasis on neural networks. Readers are led...
Independently published, 2021. — 674 p. — ISBN B08V4VZSCH. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machine...
World Scientific Publishing Europe Ltd, 2024. — 240 p. — ISBN: 978-1800615021. Cutting through the mass of technical literature on machine learning and AI and the plethora of fear-mongering books on the rise of killer robots, Secrets of Machine Learning offers a clear-sighted explanation for the informed reader of what this new technology is, what it does, how it works, and why...
Springer, 2024. — 287 p. — (Machine Intelligence for Materials Science). — ISBN: 978-3-031-44621-4. Focusing on the fundamentals of Machine Learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced Machine Learning and optimization methods for applications in materials modeling and discovery. The book explains complex...
BPB Publishing, 2021 — 751 p. — ISBN: 9789391030421. Master the ML process, from pipeline development to model deployment in production. 'Practical Full-Stack Machine Learning' introduces data professionals to a set of powerful, open-source tools and concepts required to build a complete data science project. This book is written in Python, and the ML solutions are...
Manning Publications, 2023. — 354 p. — ISBN: 978-1-61729-713-7. Ensemble Machine Learning combines the power of multiple Machine Learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations. Sophisticated...
O’Reilly Media, 2021. — 464 p. — ISBN: 978-1-098-10236-4. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn proven ML techniques to solve a variety of image problems, including classification, object detection, autoencoders, image generation, counting, and captioning. This book...
Wiley, 2021. — 216 p. — ISBN: 978111968236. Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series...
IGI Global, 2020. -364 p. - ISBN: 9781799830955 Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a...
BPB Publications, 2023. — 380 p. A practical guide to mastering Classification algorithms for Machine Learning. Key Features. Get familiar with all the state-of-the-art classification algorithms for machine learning. Understand the mathematical foundations behind building machine learning models. Learn how to apply machine learning models to solve real-world industry problems....
Boca Raton: CRC Press, 2025. — 361 p. Machine Learning (ML) algorithms have shown high accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals....
Apress, 2021. — 284 p. — ISBN: 978-1484270226. Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights)....
CRC Press, 2022. — 212 p. — ISBN: 978-0-367-63453-7. The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature,...
John Wiley & Sons, 2021. — 464 p. — (For Dummies). 2nd Edition Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android?as in the movie Ex Machina?it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving...
Boston: The MIT Press, 2021. — 863 p. A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered...
Apress, 2024. — 631 p. — ISBN-13 979-8-8688-0601-8. The role of data engineering has become increasingly crucial. Data engineering is the foundation on which organizations build their reporting, analytics, and machine learning capabilities. Data engineering as a discipline transforms raw data into reliable and insightful informational resources that provide valuable insights to...
Elsevier, 2023. — 448 p. — ISBN: 978-0443187649. Applications of Artificial Intelligence in Mining, Geotechnical, and Geoengineering provide recent advances in mining, geotechnical, and geoengineering, as well as applications of artificial intelligence in these areas. It serves as the first book on applications of artificial intelligence in mining, geotechnical, and...
BPB Publications, 2019 — 280 p. — ISBN: 9789389328127. Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite...
Ayush Rastogi, Sribharath Kainkaryam, Srimoyee Bhattacharya, Luigi Saputelli. — Apress Media LLC., 2020. — 320 p. — ISBN13: 978-1-4842-6093-7. Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the...
Basel: Birkhäuser, 2022. — 160 p. This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks. The author demonstrates how the concept of reproducing kernel Hilbert Spaces (RKHS), accompanied by tools from regularization theory, can be effectively...
Third Release: O’Reilly Media, 2022. — 180 p. — ISBN13: 9781098106829. Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. If you're looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is...
Springer Cham, 2023. — 451 p. — (Computational Methods in Engineering & the Sciences) — eBook ISBN: 978-3-031-36644-4. Comprehensive state-of-the-art book on scientific machine learning approaches in modeling & simulation. Covers the wide range of PDEs, uncertainty, optimization, inverse analysis, constitutive modeling & material design. Focuses on engineering applications in...
Springer, 2022. — 495 p. This book organizes principles and methods of signal processing and Machine Learning into the framework of coherence. The book contains a wealth of classical and modern methods of inference, some reported here for the first time. General results are applied to problems in communications, cognitive radio, passive and active radar and sonar, multi-sensor...
Nova Science Publishers, Inc., 2021. — 186 p. — ISBN: 153619512X. The machine learning field is concerned with the question of how to create computer programs that automatically improve information. In recent years, many successful electronic learning applications have been made, from data mining systems that learn to detect fraudulent credit card transactions, filtering...
Boca Raton: CRC Press, 2025. — 367 p. This book discusses state-of-the-art reviews of the existing Machine Learning techniques and algorithms including hybridizations and optimizations. It covers applications of Machine Learning via Artificial Intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition...
AMKCORP Academics, 2024. — 212 p. — ISBN: 978-81-970871-3-4. The book titled “The Science of Machine Learning Part 1” serves as a comprehensive guide for both beginners and experienced practitioners in the field of Machine Learning. Covering a wide range of topics, the book provides a thorough introduction to the fundamentals of Machine Learning, as well as advanced techniques...
Cambridge Scholars Publishing, 2022. — 365 p. — ISBN: 978-1-5275-8324-5. This book is a collection of real-world cases, illustrating how to handle challenging and volatile financial time series data for a better understanding of their past behavior and robust forecasting of their future movement. It demonstrates how the concepts and techniques of statistical, econometric,...
Independently published, 2022. — 240 p. — ISBN-10: B09HKHPT9M. Why was this book written? Machine learning is a vast topic if you look at the various disciplines originating from it. You will also hear buzzwords such as AI, Neural Networks, Deep learning, and AI Engineering being associated with machine learning. Our aim in this book is to demystify these concepts and provide...
3rd ed. — Singapore: World Scientific Publishing Company, 2024. — (Series on Intelligence Science, 5). — 390 p. — ISBN: 9811293988. This third edition comprehensively captures the cutting-edge research achievements of AI. Topics are thoroughly revised and updated, presenting the latest techniques and strategies to address the impending challenges facing computer scientists...
Scrivener Publishing, 2022 — 480 p. — ISBN: 1119821258. The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning has played a main role in software...
O’Reilly Media, 2023. — 450 p. — ISBN: 978-1-098-12020-7. Edge artificial intelligence is transforming the way computers interact with the real world, allowing Internet of Things (IoT) devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can...
John Wiley & Sons, Inc., 2025. — 656 p. — ISBN-13: 978-1394155378. A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering...
MIT Press, 2012. — 263 p. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural...
New York: Nova Science Publ., 2021. — 370 p. — (Computer Science, Technology and Applications). — ISBN: 978-1-68507-0. Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python describes the deep learning models and ensemble approaches applied to decision-making problems. The authors have addressed the concepts of deep...
Leanpub, 2020. — 220 p. The software industry is experiencing a boom in ML development and usage. This is not unlike previous software engineering booms in the early 2000s. The current boom manifests itself with a menagerie of constructs, abstractions, frameworks, and workflows. This multitude of integration challenges reminds us of old and classic software problems. Some of...
O’Reilly Media, Inc., 2024. — 402 p. — ISBN: 978-1-098-14463-0. Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery practically and simply. David Tan, Ada Leung, and...
Packt Publishing, 2023. — 334 p. The AI winter has long thawed, but many organizations are still failing to harness the power of machine learning (ML). If you want to tap that potential and add value to your business with cutting-edge emotion analysis, you’ve found what you need in this trusty guide. In Machine Learning for Emotion Analysis, you’ll take your foundational data...
Scatterplot Press, 2024. — 145 p. — ISBN: 9781835461969. Unlock the secrets of data science and machine learning with our comprehensive Python course, designed to take you from basics to complex algorithms effortlessly. Key Features Navigate through Python's machine-learning libraries effectively. Learn exploratory data analysis and data scrubbing techniques. Design and...
2nd Edition. — Academic Press, 2020. — 1160 p. — ISBN: 9780128188033. This book, Second Edition, gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches based on optimization techniques combined with the Bayesian inference approach. The book builds from the basic classical methods to recent trends, making it suitable for...
CRC Press, 2022. — 330 p. — ISBN: 978-1-032-03950-3. This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian...
Ryan Turner, 2019. — 144 p. Are you a novice programmer who wants to learn Python Machine Learning? Are you worried about how to translate what you already know into Python? This book will help you overcome those problems! As machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve...
Berlin: Walter de Gruyter, 2024. — 210 p. — (De Gruyter Textbook). — ISBN 3111288471. This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms,...
BPB Publications, 2023. — 302 p. — ISBN: 978-93-89898-27-9. A complete guide that will help you get familiar with Machine Learning models, algorithms, and optimization techniques. Key Features Understand the core concepts and algorithms of Machine Learning. Get started with your Machine Learning career with this easy-to-understand guide. Discover different Machine Learning use...
Routledge, 2023. — 219 p. Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes, approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans. Providing an exploration of the common ground between human and machine learning and of the...
CRC Press, 2023. — 473 p. — (Textbooks in Mathematics) — eBook ISBN: 978-0-367-42551-7. The primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of...
CRC Press, 2024. — 287 p. Dr Pawan Whig, Dean of Research, Vivekananda Institute of Professional Studies-TC, India. Dr Pavika Sharma, Assistant Professor, Bhagwan Parshuram Institute of Technology, India. Dr Nagender Aneja, Program Leader (Computer Science), Universiti Brunei Darussalam, Brunei. Dr Ahmad A. Elngar, Head of Computers and AI, Beni-Suef University, Egypt. Dr Nuno...
Independently published, 2020. — 335 p.— ISBN B08RCSJ39K. Mastering Deep Learning with Python: 2 Manuscripts This book presents the knowledge and technologies that will allow us to participate in this new era of information, governed by Big Data and machine learning, the life of the data is analyzed step by step, showing how to obtain it, store it, process it, visualize it, and...
Chapman and Hall/CRC, 2023. — 428 p. Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real-world problem. This book tackles this challenge through model-based machine learning...
Boca Raton: CRC Press, 2023. — 468 p. — ISBN: 1498756816. Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real-world problem. This book tackles this challenge through model-based...
Wiley, 2022. — 585 p. — ISBN: 9781119716747. Discover a thorough exploration of how to use computational, algorithmic, statistical, and informatics methods to analyze digital data Informatics and Machine Learning: From Martingales to Metaheuristics delivers an interdisciplinary presentation on how to analyze any data captured in digital form. The book describes how readers can...
Springer, 2015. — 284 p. The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. This approach is able to utilize complex, diverse and high-dimensional data sets, which often occur in manufacturing applications, and to integrate the important process intra- and interrelations. The approach has...
Packt Publishing, 2023. — 270 p. Key Features Design, build, and run microservices systems that utilize the full potential of machine learning. Discover the latest models and techniques for combining microservices and machine learning to create scalable systems. Implement machine learning in microservices architecture using open-source applications with pros and cons. Book...
Princeton: Princeton University Press, 2023. — 277 p. — ISBN: 069120392X. A hands-on introduction to machine learning and its applications to the physical sciences. As the size and complexity of data continue to grow exponentially across the physical sciences, machine learning is helping scientists sift through and analyze this information while driving breathtaking advances in...
Teaching handbook. — Ekaterinburg: Ural University Press, 2024. — 84 p. The book introduces the fundamental principles of various aspects of data analysis, providing students with a foundation in preparing them for further tasks using tools such as Python for machine learning. The main data analysis tools are considered: SQL, Python, Power BI, and the Git version control...
Springer, 2020. - 121 p. - ISBN: 9813340215 This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly...
Prateek Agrawal, Charu Gupta, Anand Sharma, Vishu Madaan, Nisheeth Joshi. — John Wiley & Sons, Inc., 2022. — 271 p. — ISBN: 978-1119775614. Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia. Machine learning (ML)...
CRC Press, 2021. — 339 p. — ISBN: 978-0367336745. The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book...
IGI Global, 2023. — 385 p. — ISBN-13: 978-1668456439. In recent years, artificial intelligence (AI) has drawn significant attention concerning its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many...
CRC Press, 2023. — 409 p. — ISBN: 978-0-367-77205-5. This book presents some post-estimation and prediction strategies for the host of useful statistical models with applications in Data Science. It combines statistical learning and Machine Learning techniques uniquely and optimally. It is well-known that Machine Learning methods are subject to many issues relating to bias, and...
O’Reilly Media, Inc., 2018. — 366 p. — ISBN: 978-1-491-98938-8. This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or...
O’Reilly, 2020. — 260 p. — ISBN: 149204511X. Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the...
Independently published, 2024. — 136 p. (converted to PDF). This comprehensive guide is tailored to address precisely those concerns and assist you in navigating through the intricacies of Python Machine Learning. In "Python Machine Learning: A Comprehensive Beginner's Guide with Scikit-Learn and Tensorflow," you will embark on a journey to unravel the mysteries of:...
Boca Raton: CRC Press, 2024. — 169 p. — ISBN: 1032515813. Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models. As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand...
Springer, 2024. — 163 p. This book deals with the exciting, seminal topic of Online Machine Learning (OML). The content is divided into three parts: the first part looks in detail at the theoretical foundations of OML, comparing it to Batch Machine Learning (BML) and discussing what criteria should be developed for a meaningful comparison. The second part provides practical...
Springer, 2023. — 327 p. Hyperparameter tuning? Is this relevant in practice? Is it not rather an academic gimmick? This book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of Machine Learning (ML) and Deep Learning (DL) methods. Programming code is provided so that...
Springer, 2023. — 327 p. — ISBN: 978-981-19-5169-5. Hyperparameter tuning? Is this relevant in practice? Is it not rather an academic gimmick? This book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of Machine Learning (ML) and Deep Learning (DL) methods....
2nd Edition. — Wiley, 2020. — 420 p. — ISBN: 978-1-119-64225-1. Dig deep into the data with a hands-on guide to machine learning with updated examples and more! This book provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant,...
The scientific article, Brown University, Quarterly of Applied Mathematics, April 1958, vol. 16, No 1, 87 - 90 p. Formulation. Functional equation approach. Uniqueness. Approximation in policy space. Computational aspects. Monotone increasing convergence.
New York: Springer, 2021. — 197 p. This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and...
Gulf Professional Publishing, 2021. — 461 p. — ISBN: 978-0128219294. Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves...
CRC Press, 2023. — 308 p. This book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts...
Groningen: University of Groningen Press, 2023. — 292 p. — ISBN: 9789403430270. The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus...
Albert Bifet, Ricard Gavalda, Geoff Holmes, Bernhard Pfahringer. — MIT Press, 2018. — 288 p. — ISBN: 978-0-262-03779-2. Machine Learning for Data Streams: with Practical Examples in MOA (Adaptive Computation and Machine Learning series) A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available...
CRC Press, 2020. - 488 p. - ISBN: 9781138495685 Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which...
Busharest: GAER Publishing House, 2020. — 76 p. In Machine Learning (ML), support-vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. This book covers in the first part the theoretical aspects of support vector machines and their functionality, and then based on the discussed...
Independently published, 2022. — 98 p. — ASIN: B09QYQC31S. Have you thought about a career in data science? It’s where the money is right now, and it’s only going to become more widespread as the world evolves. Machine learning is a big part of data science, and for those that already have experience in programming, it’s the next logical step. Machine learning is a subsection...
Chapman and Hall/CRC, 2022. — 154 p. — ISBN: 9781003267003. This book introduces the reader to Artificial Intelligence and its importance to our future. Campbell uses behavioral psychology, explores technology, economics, real-life and historical examples to predict five future scenarios with AI. Illustrating through speculative fiction, she describes possible futures after AI...
Mercury Learning & Information, 2020. — 339 p. — ISBN: 978-1-68392-467-8. This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectures...
Mercury Learning and inforMation LLC., 2020. — 339 p. — ISBN: 978-1-68392-467-8. This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning...
CRC Press, 2024. — 289 p. — ISBN: 978-1-032-39523-4. Machine Learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards Computer Science students. This, however, creates a complication for scientists across the physical sciences who also want to understand the main concepts of Machine Learning and look ahead to applications and...
CRC Press, Taylor & Francis Group, LLC, Enrique Garcia Ceja, 2022. — 432 p. — (Chapman & Hall/CRC The R Series). — ISBN: 978-1-032-06704-9. Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based...
Leanpub, 2021. — 374 p. Learn how to leverage the power of machine learning and deep learning to analyze behavioral patterns from sensors data and electronic records. This book shows you how to explore, preprocess, encode, and visualize your data This is the first version (Leanpub) of the book. The most recent version is available for free in html at...
Manning Publications, 2023. — 335 p. Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your Machine Learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning. Differential privacy techniques for machine learning. Privacy-preserving synthetic data generation. Privacy-enhancing...
New York: Apress, 2022. — 290 p. Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems and create new algorithms for your use.
Bentham Science Publishers, 2022. — 360 p. — ISBN: 978-1-68108-940-9. Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, including...
Wiley-IEEE Press, 2024. — 240 p. Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations. Machine Learning Applications discuss methodological advancements of Machine Learning and Deep Learning, present applications in image processing, including face and vehicle detection, image classification,...
CRC Press, 2023. — 339 p. — ISBN: 978-0367569129. Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that uses soft computing and algorithms to enable computers to learn on their and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various...
Academic Press, 2022. — xiv, 284 p. — ISBN: 978-0-12-824020-5. Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense, and verification. Sections cover the adversarial attack, verification, and defense, mainly focusing on image classification applications which are the standard...
New York: W. W. Norton & Company, 2020. — 510 p. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us?and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end,...
CRC Press, 2024. — 249 p. Data Science and Machine Learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML techniques are useful for them or, if so, which ones are applicable in...
Springer, 2021. — 173 p. — ISBN: 978-3-030-70678-4. This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The...
Independently published, 2020. — 210 p. — ISBN: 979-8611346952. As a beginner you might think that programming is complex... Learning artificial intelligence coding can take months, and the possibility to give up before mastering it could be high. So, if you have a project to develop you could think on hiring a professional developer to shorten the time. This may seem like a...
Cambridge University Press, 2022. — 408 p. — ISBN: 9781009128490. This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine...
Independently Published, 2020. — 107 p. Are you looking for an easy to follow guide for learning Python programming for Machine Learning? In this book Marvin Dale is going to teach you the basics and some advanced tricks for learning Python and Machine Learning. "Machine Learning is the future". You have surely heard that sentence, from the news or from a friend. You also have...
New York: CRC Press/Taylor & Francis Group, 2019. — 345 p. The book focuses on how machine learning and Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including...
BPB Publications, 2023. — 150 p. — ISBN-13: 978-93-55511-133. A guide to understanding the basics of Machine Learning and its application in the field of education. Key Features: Create a more efficient and effective learning environment that meets the needs of all students. Learn how to use the profound Machine learning advancements in the field of education. Understand how...
CRC Press, 2024. — 306 p. This book addresses the growing need for Machine Learning and data mining in neuroscience. The book offers a basic overview of neuroscience, Machine Learning, and the required math and programming necessary to develop reliable working models. The material is presented in an easy-to-follow user-friendly manner and is replete with fully working Machine...
Springer, 2023. — 231 p. — (Computational Intelligence Methods and Applications). — ISBN: 978-981-19-3887-0. Using metaheuristics to enhance Machine Learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via...
Authors edition. 2016. — 578 p. This Book is from the courses of two professional Data Scientists Kirill Eremenko from SuperDataScience and Hadelin de Ponteves from BlueLife AI. You can get a quick overview of Machine Learning & Deep Learning from this book. Also, this book will be the best guide for the Courses of Kirill Eremenko and Hadelin de Ponteves. It can also be helpful...
Addison-Wesley Professional, 2019. — 592 p. — (Addison-Wesley Data & Analytics Series). — ISBN: 978-0134845623. The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyonewill help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an...
Springer-Verlag Berlin Heidelberg, 2012. — 345 p. — ISBN: 978-3-540-75196-0, ISBN: 978-3-540-75197-7 (eBook). It was on a balmy spring day in Nanjing, while we were working on a paper on contrast discovery for the Journal of Machine Learning Research, that Nada Lavraˇc let slip that she was writing a book on rule learning with Johannes F¨urnkranz and Dragan Gamberger. I must...
World Scientific Publishing, 2021. — 896 p. Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems that frequently occur throughout Machine Learning. This book blends theory with practice by not only carefully discussing the mathematical underpinnings of each optimization technique but also by applying these techniques to linear programming,...
Institution of Engineering & Technology, 2023. — 258 p. — ISBN: 978-1-83953-617-5. Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications, and challenges cover the basic properties, features, and models for Earth observation (EO) recorded by very high-resolution (VHR) multispectral, hyperspectral, synthetic aperture radar (SAR), and...
Independently published, 2019. — 146 p. — ISBN13: 978-1089401513. What is machine learning? How machine learning works? Should I use a machine learning model or another approach to solve my problem? How do I implement machine learning to my problem? What are the machine learning methods I can use for my problem? How do I know my machine learning model is efficient? Are you...
Springer, 2022. — 169 p. — ISBN: 978-3-031-14633-6. The power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of research publications using machine learning in some of the top journals in combination with an increasing number of academic...
Addison-Wesley Professional, 2018. — 272 p. - ISBN: 0134863860 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results — even if you don’t have a...
Springer, 2023. — 320 p. — ISBN: 978-3-031-19638-6. This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that are highly efficient for the diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with...
EGEA Spa - Bocconi University Press, 2020. — 200 p. — ISBN: 9788831322041. If you are looking for an engaging book, rich in learning features, which will guide you through the field of Machine Learning, this is it. This book is a modern, concise guide of the topic. It focuses on current ensemble and boosting methods, highlighting contemporray techniques such as XGBoost (2016),...
Basel: MDPI AG, 2021. — 186 p. The modeling and processing of empirical data are one of the main subjects and goals of statistics. Nowadays, with the development of Computer Science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful...
CRC, 2020. — 525 p. — ISBN: 978-1138484696. The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from...
London: Academic Press, 2022. — 398 p. Statistical Modeling in Machine Learning: Concepts and Applications present the basic concepts and roles of statistics, exploratory data analysis, and machine learning. The various aspects of Machine Learning are discussed along with the basics of statistics. Concepts are presented with simple examples and graphical representations for a...
The MIT Press, 2020. — 236 p. — ISBN: 9780262538701. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through...
Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, Ilan Filonenko. — O’Reilly Media, Inc., 2020. — 264 p. — ISBN: 978-1-492-05012-4. If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data...
Springer, 2024. - 179 p. - (Machine Learning: Foundations, Methodologies, and Applications). - ISBN: 9819706874. Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and...
CRC Press, 2023. — 268 p. — ISBN: 978-1-032-37426-0. This book aims at tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. It presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration. Analyzing the limitations of conventional...
Springer, 2021. — 293 p. — ISBN: 978-3030712693. This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select...
Samuel Hack, 2020. — 168 p. Your Free Gift Machine Learnini for Beginners The Purpose of This Book What is Artificial Intelligence? How is machine learning used? Recent Advancements in Data Analysis Introduction to Statistics Choosing the right kind of model for machine learning Supervised learning Classifications Unsupervised Learning Neural networks Reinforcement Learning...
New York: O’Reilly Media, Inc., 2021. — 112 p. Who Should Read This Book What Readers Will Learn Preliminary Book Outline. Bringing it All Together. Conventions Used in This Book. Using Code Examples. O’Reilly Online Learning. How to Contact Us. Contemporary Model Governance. Basic Legal Obligations. AI Incidents. Organizational and Cultural Competencies for Responsible AI....
New York: Morgan & Claypool, 2021. — 161 p. Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into Deep Learning (DL) architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge...
The MIT Press, 2007. — 119 p. I had the great privilege of taking the class upon which this book was based last semester at Princeton University under professors Harman and Kulkarni. It is a fascinating little book, which manages to distill decades of debate and research into concise, readable chapters that carry the presentation forward. The authors' approach is original but...
O’Reilly, 2019. — 320 p. — ISBN: 1492047546. Matt Harrison, "Machine Learning Pocket Reference: Working with Structured Data in Python" With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a...
New York: Springer, 2021. — 392 p. Today, it is all about the connected smarter world based on the new emerging technologies and Artificial Intelligence (AI) for sustainable development. Sustainable development purposes have been designed for meeting the human development goals while simultaneously sustaining the ability of natural resources in order to maintain the natural...
Springer, 2012. — 605 p. First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012, Proceedings. This book constitutes the refereed proceedings of the First International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2012, held in Cairo, Egypt, in December 2012. The 58 full papers presented were carefully reviewed and selected...
Boston: MIT Press, 2017. — 412 p. A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a...
Birmingham: Packt Publishing, 2022. — 442 p. — ISBN: 1801813574. Learn the principles of quantum machine learning and how to apply them in finance. Key Features Discover how to solve optimization problems on quantum computers that can provide a speedup edge over classical methods. Use methods of analog and digital quantum computing to build powerful generative models. Create...
CRC Press, 2022. — 295 p. — ISBN: 978-0-367-68545-4. Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and...
Packt Publishing, 2019. — 456 p. — ISBN: 1789136364. A guide to advances in machine learning for financial professionals, with working Python code. Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind...
CRC Press, 2022. — 207 p. This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity. This book focuses on real-world industrial applications and case...
New York: Springer, 2022. — 179 p. This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key...
Birmingham: Packt Publishing, 2022. - 551 p. - ISBN: 1803246804. Build real-world time series forecasting systems which scale to millions of time series by mastering and applying modern concepts in machine learning and deep learning. Key Features Explore industry-tested machine learning techniques to forecast millions of time series. Get started with the revolutionary paradigm...
2nd ed. — Birmingham: Packt Publishing, 2024. — 655 p. — ISBN: 1835883184. Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures. Key Features Apply ML and global models to improve forecasting accuracy through...
2nd Edition. — Springer, 2023. — 279 p. The new edition of this popular professional book on Artificial Intelligence (ML) and Machine Learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective,...
Springer, 2022. — 136 p. — (Synthesis Lectures on Learning, Networks, and Algorithms). — ISBN: 3031190661. This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of...
Springer, 2023. — 306 p. — ISBN: 978-981-99-0392-4. This book presents recent advancements in machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to...
Springer, 2022. — 233 p. — (Machine Learning: Foundations, Methodologies, and Applications). — ISBN: 9811681929. Machine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles. This book approaches ML as the...
Wiley, 2020. — 460 p. — ISBN: 1786305445. …This groundbreaking book on Machine Learning for Asset Management represents a refreshing collaborative effort between sophisticated investment practitioners and researchers, to present practical application of machine learning methodologies. As one can see from the different chapters, machine learning can be applied to different parts...
Wiley-Scrivener, 2024. — 327 p. — (Artificial Intelligence and Soft Computing for Industrial Transformation). — ISBN: 978-1-394-23392-2. The book unlocks the power of nature-inspired optimization in Machine Learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization...
Wiley-Scrivener, 2024. — 327 p. — (Artificial Intelligence and Soft Computing for Industrial Transformation). — ISBN: 978-1-394-23392-2. The book unlocks the power of nature-inspired optimization in Machine Learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization...
Springer, 2021. — 328 p. — ISBN: 978-3-030-83355-8. This book is written both for readers entering the field and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as...
Packt, 2018. - 356 p. - ISBN: 9781788998246 A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript Key Features Solve complex computational problems in browser with JavaScript Teach your browser how to learn from rules using the power of machine learning Understand discoveries on web interface and API in machine learning...
O’Reilly Media, Inc. 2023. — 122 p. — (2nd Early Release). — ISBN: 978-1-492-09761-7. Whether based on academic theories or machine learning strategies, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of...
Boca Raton: CRC Press, 2022. — 261 p. Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modeling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modeling, regularized regression, support vector machine, neural network,...
2nd Edition. — Apress Berkeley, 2023. — 661 p. — eBook ISBN: 978-1-4842-9801-5. This new and updated edition takes you through the details of machine learning to give you an understanding of cognitive computing, IoT, big data, AI, quantum computing, and more. The book explains how machine learning techniques are used to solve fundamental and complex societal and industry...
IGI Global, 2020. — 314 p. — ISBN: 9781799801832. Machine Learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and...
Now Foundations and Trends, 2024. — 186 p. — ISBN: 978-1-63828-317-1. Over the last decade, the long-running endeavor to automate high-level processes in machine learning (ML) has risen to mainstream prominence. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of...
Pearson Education, 2019. — 282 p. — ISBN: 978-0-13-411654-9. Foundational Hands-On Skills for Succeeding with Real Data Science Projects Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more...
Newark: John Wiley & Sons, Incorporated, 2018. — 330 p. Organization. How to Use This Book About the Companion Website. Introductionâ. Examples from Real Life. The Problem of Learning. Domain. Range. Data. Loss. Risk. The Reality of the Unknown Function. Training and Selection of Models, and Purposes of Learning. Regression. General Framework. Loss. Estimating the Model...
Packt Publishing, 2025. — 512 p. Key Features. Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries. Implement practical machine learning and deep learning techniques to build smart models. Deploy machine learning models to work on mobile and embedded devices. Book Description: Written by a seasoned software engineer...
Apress, 2021. — 296 p. — ISBN: 978-1-4842-7149-0. Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a...
Boca Raton: CRC Press, 2024. — 363 p. Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amounts of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process part of the data locally by...
Boca Raton: CRC Press, 2025. — 308 p. Symbolic Regression (SR) is one of the most powerful Machine Learning (ML) techniques that produce transparent models, searching the space of mathematical expressions for a model that represents the relationship between the predictors and the dependent variable without the need to make assumptions about the model structure. Currently, the...
Apress, 2020. - 144 p. - ISBN: 1484259394 Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices...
Leanpub, 2023. — 208 p. This book provides a comprehensive coverage of Machine Learning (ML) methods that have proven useful in process industry for dynamic process modeling. Step-by-step instructions, supported with industry-relevant case studies, show (using Python) how to develop solutions for process modeling, process monitoring, etc., using classical and modern methods....
Springer, 2021. — 349 p. — ISBN: 9789811594915. This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in dealing with these...
Wiley-Scrivener, 2024. — 385 p. This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using Machine Learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of...
Springer, 2020. — 591 p. — (Springer Series in the Data Sciences). — ISBN: 978-3-030-39567-4 This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the...
Chapman and Hall/CRC, 2024. — 369 p. — ISBN: 978-1003367147. This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teaching machine learning, emphasizing practical applications over complex mathematical concepts. Students are not required to have advanced...
CRC Press, 2018. — 349 p. — ISBN: 978-0-8153-5622-6. Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial...
Cambridge: Cambridge University Press, 2020. — 537 p. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and...
Second Edition. — Independently published, 2021. — 281 p. — ISBN 978-1838132217. This Second Edition improves clarity of code, general formatting, text explanations as well as updating the AI for 2021 stock selection. Build Your Own AI Investor Breaks down Value Investing for the AI revolution, whilst being accessible to anyone, even if you've never invested in stocks or coded...
Singapore: World Scientific, 2022. — 249 p. The book presents the confluence of wearable and wireless inertial sensor systems, such as a smartphone, for deep brain stimulation for treating movement disorders, such as essential tremors, and machine learning. Machine learning distinguishes between distinct deep brain stimulation settings, such as 'On' and 'Off' status. This...
BPB Publications, 2023. — 271 p. Learn how to use AutoML to leverage Machine Learning for solving business problems. Key Features. Get familiar with the common machine learning problems and understand how to solve them. Understand the importance of different types of data and how to work with them effectively. Learn how to use machine learning and AutoML tools to solve...
Springer, 2020. — 285 p. — ISBN: 9811529094. This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the...
3rd edition. — Packt Publishing, 2020. — 527 p. — ISBN 9781800209718. A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key Features Dive into machine learning algorithms to solve the complex challenges faced by data scientists...
Dr. Wasim Akram Mandal. — 2025. — 101 p. Artificial intelligence is evolving at an unprecedented pace, and breakthroughs continue to reshape the way we interact with technology. While OpenAI's ChatGPT has dominated the AI space, a new contender has emerged — DeepSeek AI, an innovative and powerful language model that challenges the status quo. This book, Mastering DeepSeek AI:...
Packt Publishing, 2023. — 240 p. Key Features: Master Conformal Prediction, a fast-growing ML framework, with Python applications. Explore cutting-edge methods to measure and manage uncertainty in industry applications. The book will explain how Conformal Prediction differs from traditional machine learning. Book Description: In the rapidly evolving landscape of machine...
Cambridge University Press, 2020. — 152 p. — ISBN: 1108792898. Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this...
Springer, 2022. — 254 p. This book introduces machine learning and its applications in smart environments/cities. At this stage, a comprehensive understanding of smart environment/city applications is critical for supporting future research. This book includes chapters written by researchers from different countries across the globe and identifies critical threads in research...
Independently published, 2023. — 72 p. — ASIN: B0BRW8J2FG. Machine Learning has revolutionized the field of quantitative trading, enabling traders to develop and implement sophisticated trading strategies that leverage large amounts of data and advanced modeling techniques. In this book, we provide a comprehensive overview of Machine Learning for quantitative trading, covering...
Artech House, 2021. — 349 p. — ISBN: 978-1-63081-775-6. This practical resource provides an overview of machine learning (ML) approaches as applied to electromagnetics and antenna array processing. Detailed coverage of the main trends in ML, including uniform and random array processing (beamforming and detection of angle of arrival), antenna optimization, wave propagation,...
Academic Press/Elsevier, 2023. — 222 p. Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC-based samplers. The...
Birmingham: Packt Publishing, 2021. — 311 p. — ISBN: 1800567685. Follow a hands-on approach to AutoML implementation and associated methodologies and get to grips with automated machine learning Key Features Get up to speed with AutoML using the platform of your choice, such as OSS, Azure, AWS, or GCP. Eliminate mundane tasks in data engineering and reduce human errors in ML...
Packt Publishing, 2024. — 176 p. — ISBN: 978-1835464946. Use active machine learning with Python to improve the accuracy of predictive models, streamline the data analysis process, and adapt to evolving data trends, fostering innovation and progress across diverse fields. Key Features Learn how to implement a pipeline for optimal model creation from large datasets and at lower...
Cambridge University Press, 2022 — 337 p. — ISBN: 1316518906. Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that...
Springer, 2021. — 252 p. — ISBN: 978-3-030-70387-5. All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data-intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines — mechanical, aerospace,...
Springer, 2024. — 351 p. — ISBN: 978-3-031-50713-7. The book systematically expounds the main results obtained by the author in the field of design and optimization of high-speed integrated circuits (ICs) and their standard blocks (heterogeneous ICs, analog-to-digital and digital-to-analog converters, input/output cells, etc.) operating in non-standard conditions (deviations of...
Ethem Mining, 2020. — 177 p. What is Machine Learning? Definition of Machine Learning History of Machine Learning The Future of Machine Learning Application of Machine Learning Technology Industry Agricultural Industry Medical Industry Financial Industry Marketing Industry Human Behavior Industry Benefits of Machine Learning Practical Examples of Everyday Use of Machine...
World Scientific Publishing Company, 2021. — 296 p. This set of lecture notes, written for those who are unfamiliar with mathematics and programming, introduces the reader to important concepts in the field of Machine Learning. It consists of three parts. The first is an overview of the history of Artificial Intelligence (AI), Machine Learning (ML), and Data Science (DS), and...
Wiley, 2020. — 336 p. — ISBN: 978-1-119-60290-3. Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine...
CRC Press, 2023. — 379 p. — ISBN: 978-1-032-07452-8. The utilization of Machine Learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in...
Scrivener Publishing, 2021. — 528 p. — ISBN: 978-1-119-78580-4. In this era of IoT, edge devices generate gigantic data during every fraction of a second. The main aim of these networks is to infer some meaningful information from the collected data. For the same, the huge data is transmitted to the cloud which is highly expensive and time-consuming. Hence, it needs to devise...
Leanpub, 2023-02-14. — 101 p. This book teaches you how to quantify the uncertainty of machine learning models with conformal prediction in Python. Introduction To Conformal Prediction With Python is the quickest way to learn an easy-to-use and very general technique for uncertainty quantification. A prerequisite for trust in Machine Learning is uncertainty quantification....
Manning Publications, 2021. — 426 p. — ISBN: 9781617296741. Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction...
De Gruyter, 2023. — 506 p. — ISBN: 978-3-11-078593-7. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
Springer Cham, 2022. — 465 p. — (International Series in Operations Research & Management Science, volume 334). — eBook ISBN: 978-3-031-16990-8. This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward...
Independently published, 2020. — 234 p. — ASIN B08R2YCNKN. Do you know all the automatic recommendations come when you type something while searching is a case of machine learning. Isn't it magical ? But nothing is, grab this book and Get an insight of what machine learning is and not just that . Learn how to train your own models using different algorithms like regressors,...
Leanpub, 2022. — 112 p. Build a career while doing technical writing. I earned $300 for my first paid Data Science and Machine Learning article. I get paid between $250 and $500 for each Data Science article I write. In this ebook, I'll show you how you too, can earn while writing about Data Science and Machine Learning. You have been learning about Data Science and Machine...
IGI Global Scientific Publishing, 2025. — 504 p. Description: In today's rapidly evolving world, businesses are confronted with the complex task of streamlining their operations, utilizing machine learning to their advantage, and maneuvering through the intricacies of artificial intelligence. It has become increasingly essential to allocate resources effectively, make informed...
Princeton University Press, 2021 Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly...
Wiley, 2024. — 480 p. — ISBN: 978-1-394-21415-0. Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As...
Leanpub, 2022. — 92 p. Version: 2022-09-10 The book is intended to get you acquainted with the world of Supervised Machine Learning and does not assume previous knowledge of the field. The commonly leveraged Linear Regression technique used to provide predictions that are continuous in nature is detailed in the book. Sample Python code included! If you are not familiar with...
Manning Publications Co, 2021. — 493 p. — ISBN: 9781617295645. At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs....
Apress, 2021. — 192 p. — ISBN: 978-1484271094. Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern...
Wiley, 2020. — 464 p. — ISBN: 1119591511. Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning — a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions — allows organizations to...
Ally S. Nyamawe, Mohamedi M. Mjahidi, Noe E. Nnko, Salim A. Diwani, Godbless G. Minja, Kulwa Malyango. — Chapman and Hall/CRC, 2025. — 192 p. —. ISBN: 978-87-7004-713-5/. The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to...
Morgan & Claypool, 2022. — 110 p. — (Synthesis Lectures on Artificial Intelligence and Machine Learning). — ISBN: 3031791665. Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision-making to achieve long-term goals. In some cases, this machine learning approach can save programmers time,...
Packt Publishing, 2022 — 337 p. — ISBN: 1484279204. Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with...
Springer, 2021. — 177 p. — (Studies in Big Data, 87). — ISBN: 981336517X. This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9–12 January 2020 in conjunction with 16th edition of ICDCIT . The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge...
Stylianos Papadakis, Alexandros Garefalakis, Christiana Chimonaki, Christos Lemonakis, Constantin Zopounidis — IGI Global, 2021. — 330 p. — (Advances in Finance, Accounting, and Economics (AFAE)).— ISBN: 1799848051, 9781799848059. The prediction of the valuation of the quality of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in...
Genoa: University of Genoa, 2007. — 101 p. Many approaches for obtaining systems with intelligent behavior are based on components that learn automatically from previous experience. The development of these learning techniques is the objective of the area of research known as machine learning. During the last decade, researchers have produced numerous and outstanding advances...
O’Reilly Media, 2019. — 359 p. — ISBN: 9781492035640. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to...
Springer, 2022. — 394 p. — (Communications and Control Engineering). — ISBN: 978-3-030-95859-6. This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into the understanding of a novel paradigm that leverages the...
Packt Publishing, 2022 — 90 p. — ISBN: 9781801072168. Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions With a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization, so there is a huge demand for skilled ML...
O’Reilly Media, 2022. — 300 p. — ISBN-13: 978-1-098-10682-9. Get up to speed on Apache Spark, the popular engine for large-scale data processing, including machine learning and analytics. If you’re looking to expand your skill set or advance your career in scalable machine learning with MLlib, distributed PyTorch, and distributed TensorFlow, this practical guide is for you....
Springer, 2008, -375 p. 5th InternationalWorkshop, MLMI 2008, Utrecht, The Netherlands, September 8-10, 2008. Proceedings. The 32 papers gathered in this volume cover a wide range of topics related to human-human communication modeling and processing, as well as to human-computer interaction, using several communication modalities. A significant number of papers focus on the...
Chapman and Hall/CRC, 2023. — 405 p. — ISBN13: 9781003306566. Entropy Randomization in Machine Learning presents a new approach to machine learning — entropy randomization — to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy...
CRC Press, 2024. — 225 p. This book brings together a blend of different areas of Machine Learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms. Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describe the...
Xlibris US, 2021. — 556 p. This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised Machine Learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector...
Apress, 2021. — 127 p. — ISBN: 978-1912807130. Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an...
Apress, 2020. — 355 p. — ISBN: 1484256689. Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation...
Setareh Rafatirad, Houman Homayoun, Zhiqian Chen, Sai Manoj Pudukotai Dinakarrao. — Springer, 2022. — 464 p. — ISBN: 978-3-030-96755-0/ This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution...
CRC Press, 2022. — 211 p. — ISBN: 978-1-032-07401-6. Physics of Data Science and Machine Learning links fundamental concepts of physics to Data Science, Machine Learning, and Artificial Intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data...
New York: Springer, 2022. — 362 p. This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of Machine Learning communities across the world in recent years....
SAS, 2021. — 230 p. — ISBN: 9781953329622. Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data...
Manning Publications, 2020. — 537 p. — ISBN: 1617296570. Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of academic data...
Self-published - 2021 - 159 p - ASIN : B09BFBJLDK. Is it accurate to say that you are interested about machine learning and AI and you don't have the foggiest idea where to begin? Have you at any point heard individuals discussing Machine Learning however you just have a dubious thought of the real significance? Would you like to see how machine learning could improve on your...
Packt Publishing, 2024. — 280 p. Authors: Stephanie Rivera, Anastasia Prokaieva, Amanda Baker, Hayley Horn. Key Features: Build machine learning solutions faster than peers only using documentation. Enhance or refine your expertise with tribal knowledge and concise explanations. Follow along with code projects provided in GitHub to accelerate your projects. Book Description:...
New York: Amazon.com Services LLC, 2021. — 403 p. Machine learning can be broadly classified into supervised and unsupervised learning. By definition, the term supervised means that the “machine” (the system) learns with the help of something — typically a labeled training data. Training data (or a dataset ) is the basis on which the system learns to infer. An example of this...
Independently published, 2021. — 111 p. — ISBN13: 9781005466787. Artificial Intelligence (AI) is a branch of computer science that seeks to develop computer systems that are capable of human-like intelligence. You can have Artificial Intelligence that replicates the human mind implemented this way rather than just having a computer system that mimics and the entire human brain....
USA: IGI Global, 2024. — 557 p. — (Advances in Information Security, Privacy, and Ethics (AISPE) Series). – ISBN: 9798369341599. As the Editors of this comprehensive reference book, Machine Learning and Cryptographic Solutions for Data Protection and Network Security, we are delighted to present a collaborative effort aimed at bridging the realms of machine learning,...
BPB Publications, 2021. — 866 p. This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms...
Apple Academic Press, CRC Press, 2025. — 353 p. — ISBN: 978-1-77491-639-1. This new book provides an in-depth understanding of Federated Learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how Federated Learning (FL) integrates AI technologies, such as...
Nova Science Pub Inc., 2021. — 248 p. — ISBN BN-13: 9781536187571. “Support Vector Machines: Evolution and Applications reviews the basics of Support Vector Machines (SVM), their evolution and applications in diverse fields. SVM is an efficient supervised learning approach popularly used for pattern recognition, medicalimage classification, face recognition and various other...
Springer Singapore, 2024. — 127 p. — (Studies in Computational Intelligence, volume 1155). — eBook ISBN: 978-981-97-2720-9. Covers three primary data types: numerical, textual, and image data. Offers GitHub source code encompassing fundamental components and advanced machine learning tools. Serves as a reference for researchers, students, practitioners, and policymakers....
O’Reilly, 2024. — 332 p. Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process. In this hands-on guide, author Anthony...
9th release. — O’Reilly, 2023. — 304 p. — ISBN: 9781492094517. Your training data has as much to do with the success of your data project as the algorithms themselves — most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This...
BPB Publications, 2021. — 427 p. — ISBN: 9389328624, 9789389328622 Beginner's guide to explore machine learning in banking and finance. The fields of machining adapting, profound learning, and computerized reasoning are quickly extending and are probably going to keep on doing as such for a long time to come. There are many main impetuses for this, as quickly caught in this...
Orange Education Pvt Ltd, AVA, 2024. — 411 p. — ISBN: 978-81-97223-94-5. Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn. “Ultimate Machine Learning with Scikit-Learn” is a definitive resource that offers an in-depth exploration of data preparation, modeling techniques, and the theoretical foundations behind powerful machine...
CRC Press, 2023. — 356 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — ISBN: 9780429292835. Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering...
ITexLi, 2021. — 131 p. — ISBN: 183969484X 9781839694844 1839694866 9781839694868. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data...
New York: Morgan and Claypool, 2022. — 123 p. This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and...
Springer, 2023. — 175 p. — (Engineering Optimization: Methods and Applications). — ISBN: 978-981-99-7455-9. This book aims to provide a collection of state-of-the-art scientific and technical research papers related to Machine Learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering...
Nd Edition. — Wiley, 2023. — 611 p. — ISBN: 978-1119903831. An up-to-date introduction to a market-leading platform for data analysis and machine learning. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to...
Independently published, 2021. — 269 p. — ISBN B089GQM3QD. Machine learning is a computer programming technique in which software is built in such a way that it can learn new facts from itself and make decisions on its own when necessary. Machine learning (ML) is a large discipline, and this book covers a lot of ground. We attempted to cover all aspects of the subject. This...
Apress, 2020. — 446 p. — ISBN: 1484258010. Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from...
Apress, 2020. - 150 p. - ISBN: 1484265459 This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning...
Scrivener Publishing, 2022 — 480 p. — ISBN: 1119821258. The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning has played a main role in software...
Manning Publishing, 2022. - 338 p. - ISBN: 1617298050 Automated Machine Learning in Action teaches you to automate selecting the best machine learning models or data preparation methods for your machine learning tasks, so your pipelines tune themselves without needing constant input. Optimize every stage of your machine learning pipelines with powerful automation components and...
Elektor Publication, 2022. — 248 p. — ISBN: 978-3-89576-502-5. Most people are increasingly confronted with the applications of Artificial Intelligence (AI). Music or video ratings, navigation systems, shopping advice, etc. are based on methods that can be attributed to this field. The term Artificial Intelligence was coined in 1956 at an international conference known as the...
Wiley-Scrivener Publishing, 2021. — 305 p. — ISBN: 978-1-119-76885-2. Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine...
IGI Global, 2024. — 315 p. In the ever-evolving landscape of technology, Machine Learning stands as a beacon of innovation with the potential to reshape industries and redefine our daily lives. As editors of this comprehensive reference book, Methodologies, Frameworks, and Applications of Machine Learning, we are thrilled to present a compendium that encapsulates the essence of...
2nd Edition. — CRC Press, 2023. — 528 p. Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn’t prove theorems, or dwell on mathematical theory....
Springer, 2021. — 655 p. — ISBN: 9783030625818, EISBN 9783030625825. This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging...
Springer, 2020. — 292 p. — ISBN: 3030378292. This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book’s GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power...
New York: Morgan & Claypool Publishers, 2014. — 128 p. While labeled data is expensive to prepare, ever-increasing amounts of unlabeled data are becoming widely available. To adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to...
Springer, 2021. — 260 p. — ISBN: 978-981-15-7876-2. The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a...
CRC Press, 2024. — 494 p. Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in a Single Source of Error state space form, called “ADAM” (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view of forecasting and time series analysis using dynamic models, explaining how a variety of instruments can be used...
CRC Press, 2025. — 171 p. — (Innovations in Multimedia, Virtual Reality and Augmentation). — ISBN: 978-1-032-76147-3. This book explores the interdisciplinary nature of Machine Learning in multimedia, highlighting its intersections with fields such as computer vision, natural language processing (NLP), and audio signal processing. Machine Learning in Multimedia: Unlocking the...
Emerald Publishing, 2021. — 213 p. — ISBN: 978-1-80043-881-1. Machine Learning and Artificial Intelligence in Marketing and Sales explores the ideas, and the statistical and mathematical concepts, behind Artificial Intelligence (AI) and machine learning models, as applied to marketing and sales, without getting lost in the details of mathematical derivations and computer...
Boca Raton: CRC Press/Chapman & Hall, 2022. — 149 p. This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision-making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small...
Machine Learning for Absolute Beginners, second edition, published in 2017 by Oliver Theobald. 128 p. This book describes a step-by-step, hands-on machine learning project, from data collection to model evaluation and testing. At the end, you find resources for further extending the knowledge you have acquired. Suitable for everyone, including beginners or individuals with no...
Wiley-Scrivener, 2023. — 273 p. Machine Learning and Deep Learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated...
World Scientific, 2018. — 328 p. — ISBN: 9813271221. This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithms, describes advancements and applications of these machine learning techniques, and describes the problem of...
World Scientific, 2019. — 322 p. — ISBN: 9811205663. Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical...
Wiley-IEEE Press, 2025. — 384 p. — ISBN-13: 978-1394294954. A practical guide to AI applications for Simple Python and MatLAB scripts Machine Learning and AI with Simple Python and MatLAB Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular...
Springer, 2024. — 365 p. This book delves into the practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of Machine Learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind...
2nd Edition. — Leanpub, 2020. — 272 p. This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. The skills taught in this book will lay the foundation for you to advance your journey to Machine Learning Mastery! Deep Learning has...
Reactive Publishing, December 28, 2023. — 371 p. Dive into the world of Artificial Intelligence with "Unsupervised Machine Learning with Python," the essential guide for professionals eager to master the most sophisticated analysis skills and unlock new dimensions of data interpretation. Building on the knowledge foundation of those who have already ventured into the realm of...
Apress Media LLC., 2020. — 392 p. — ISBN13: 978-1-4842-6155-2. Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and...
Palgrave Macmillan, 2023. — 182 p. Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech, and Telecommunications. Travel has been slow in adoption, but...
Springer, 2018. — 249 p. This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper...
CRC Press, 2020. — 204 p. — ISBN13: 978-1-138-32822-8. In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have...
Springer, 2024. — 141 p. In both the database and Machine Learning communities, data quality has become a serious issue that cannot be ignored. In this context, we refer to data with quality problems as “dirty data.” Clearly, for a given data mining or Machine Learning task, dirty data in both training and test datasets can affect the accuracy of results. Accordingly, this book...
Boca Raton: CRC Press, 2024. — 377 p. While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding applying ML-based modeling techniques in thin film...
Birmingham: Packt Publishing, 2022. — 395 p. — ISBN: 1801074526. Accelerate the adoption of machine learning by automating away the complex parts of the ML pipeline using H2O. ai Key Features Learn how to train the best models with a single click using H2O AutoML. Get a simple explanation of model performance using H2O Explainability. Easily deploy your trained models to...
Independently published, 2021. — 171 p. — ISBN: 979-8724724272. Machine Learning is rapidly changing the world and it is the way of the Future of Human Technology. Art, information, processes, calculations, emotions will be rapidly learned and discover from machines. Do you want to learn more about the world of Machine Learning and its Applications? Would you like to improve...
Packt, 2022. — 408 p. — ISBN: 1801811911, 9781801811910. Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key Features Learn the applications of machine learning in biotechnology and life science sectors Discover exciting real-world applications of deep learning and natural language...
Springer, 2023. — 314 p. A critical challenge in Deep Learning is the vulnerability of Deep Learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behavior of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in...
Apress Media LLC., 2020. — 262 p. — ISBN13: 978-1-4842-5771-5. Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning...
London: BPB Publications, 2023. — 306 p. — ISBN: 9355518447. Learn how to deploy complex machine-learning models on single-board computers, mobile phones, and microcontrollers. Key Features. Gain a comprehensive understanding of TinyML's core concepts. Learn how to design your TinyML applications from the ground up. Explore cutting-edge models, hardware, and software platforms...
Birmingham: Packt Publishing, 2022. — 305 p. — ISBN: 1803246154. Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems. Key Features Explore various explainability methods for designing robust and scalable explainable ML systems. Use XAI frameworks such as LIME and SHAP...
Singapore: World Scientific Publishing Company, 2024. — 545 p. — ISBN: 981129254X. Authored by researchers and practitioners who build cutting-edge federated learning applications to solve real-world problems, this book covers the spectrum of federated learning technology from concepts and application scenarios to advanced algorithms and finally system implementation in three...
Arcler Press, 2021-12-01. — 264 p. — ISBN: 978-1-77469-233-2. Deep learning and machine learning have gained significant importance in the last few years. New inventions and discoveries are taking place every day to exploit the concepts of machine-learning techniques. This book aims to present the fundamentals of machine learning with an emphasis on deep learning, neural...
Springer, 2021. - 108 p. - ISBN: 9783030683788 This book presents a system that combines the expertise of four algorithms, namely Gradient Tree Boosting, Logistic Regression, Random Forest and Support Vector Classifier to trade with several cryptocurrencies. A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used...
Cambridge University Press, 2009. — 716 p. — ISBN: 978-0-521-83378-3. This book is about convex optimization, a special class of mathematical optimization problems, which includes least-squares and linear programming problems. It is well known that least-squares and linear programming problems have a fairly complete theory, arise in a variety of applications, and can be solved...
Mercury Learning & Information, 2020. — 261 p. — ISBN: 1683924703 As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic machine learning concepts and incorporate that knowledge into Angular applications. The book is intended to be a fast-paced introduction to some basic features of machine learning and an overview of several...
Mercury Learning and Information, 2024. — 169 p. This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an assortment of Python 3.x code...
Mercury Learning & Information, 2020. — 363 p. — ISBN: 978-1-68392-495-1. This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is...
Packt, 2020. — 256 p. — ISBN: 9781789801781. Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key Features Get well-versed with the ML.NET framework and its components and APIs using practical examples Learn how to build, train, and evaluate popular machine learning...
Packt, 2021 — 344 p. — ISBN: 1801076804, 9781801076807. Leverage DataRobot's enterprise AI platform and automated decision intelligence to extract business value from data Key Features Get well-versed with DataRobot features using real-world examples Use this all-in-one platform to build, monitor, and deploy ML models for handling the entire production life cycle Make use of...
Springer, 2023. — 241 p. — ISBN: 978-3-031-31010-2. This book introduces the concept of “bespoke learning”, a new mechanistic approach that makes it possible to generate values of an output variable at each designated value of an associated input variable. Here the output variable generally provides information about the system’s behavior/structure, and the aim is to learn the...
Scrivener Publishing LLC., Wiley, 2022. — 470 p. — (Advances in Learning Analytics for Intelligent Cloud-IoT Systems). — ISBN: 978-1-119-76225-6. This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to...
Academic Press/Elsevier, 2024. — 520 p. Recently, Tiny Machine Learning (TinyML) has gained incredible importance due to its capabilities of creating lightweight machine learning (ML) frameworks aiming at low latency, lower energy consumption, lower bandwidth requirement, improved data security and privacy, and other performance necessities. As billions of battery-operated...
Wiley, STE Ltd., 2022. — 255 p. — (Computer Science, Operational Research and Decision). — ISBN: 978-1-78945-071-2. Machine Learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. This book aims to present some of the innovative techniques in the field of...
Springer Cham, 2024. — 119 p. — (Intelligent Systems Reference Library, volume 255). — eBook ISBN: 978-3-031-59811-1. Presents the novel logo detection methods using machine learning paradigms. Demonstrates the merits of the presented approaches over the reported approaches using real-world applications. Includes the state-of-the-art machine learning paradigms. This book...
2021, 244 p. Many Machine Learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely...
Packt Publishing, 2019. — 325 p. — ISBN: 978-1-78980-794-3. R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within...
Anderson Coen, 2019. — 180 p. What is machine learning? Does it really help businesses provide better services and earn more? How can I improve my business processes to increase ROI (Return On Investment)? I am unable to focus on important tasks because I am getting bogged down by menial tasks. If you are confronted by one or more of these questions, this book is for you! You...
Birmingham: Packt Publishing, 2023. — 679 p. — ISBN: 1804613835. Work with fully explained algorithms and ready-to-use examples that can be run on quantum simulators and actual quantum computers with this comprehensive guide. Key Features Get a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisites. Learn the process of...
Morgan & Claypool, 2022. — 190 p. — (Synthesis Lectures on Artificial Intelligence and Machine Learning). — ISBN: 1636393438. Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of...
CRC, 2020. — 262 p. — ISBN: 9780367503369. The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature...
New York: Springer, 2022. — 202 p. This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different applications of machine learning techniques for industrial...
CRC Press, 2020. — 280 p. — ISBN13: 978-1-138-32339-1. Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both...
Singapore: Springer, 2023. — 119 p. Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular...
Springer, 2021. — 162 p. — ISBN: 978-3-030-83212-4. This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather, and natural disasters...
Springer, 2012. — 81 p. — ISBN: 978-1-4471-2977-6, e-ISBN: 978-1-4471-2978-3. This chapter presents a brief introduction to entropy-guided transformation learning (ETL), a machine learning algorithm for classification tasks. ETL generalizes transformation-based learning (TBL) by automatically solving the TBL bottleneck: the construction of good template sets. The main advantage...
New York: Springer, 2018. — 286 p. This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides...
Springer, 2023. — 253 p. — (Data Science, Machine Intelligence, and Law 02). — ISBN: 978-3-031-19666- This book gives a comprehensive overview of the state of Artificial Intelligence (AI), especially Machine Learning (ML) applications in public service delivery in Estonia, discussing the manifold ethical and legal issues that arise under both European and Estonian law. Final...
Hoboken: Wiley, 2022. — 482 p. Rank-Based Methods for Shrinkage and Selection. A practical and hands-on guide to the theory and methodology of statistical estimation based on rank. Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes...
Manning Publications, 2020. — 220 p. — ISBN: 978-1617294884. How Machine Learning Works gives you an in-depth look at the mathematical and theoretical foundations of machine learning. Seasoned practitioner Mostafa Samir Abd El-Fattah takes you step by step through a real-world ML projects. In it, you’ll learn the components that make up a machine learning problem and explore...
Springer, 2023. — 145 p. — (The Information Retrieval Series 47). — ISBN: 978-3031204661. This open-access book provides an introduction and an overview of learning to quantify (a.k. a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data using supervised learning. In data science, learning to quantify is a task of its related to...
New York: Nova Science Publishers, Inc., 2021. — 810 p. — (Computer Science, Technology and Applications). — ISBN: 978-1-53619-813-3. With the emergence of revolutionary technological standards such as 5G and Industry 4.0, real-time applications that require both cloud computing and machine learning are becoming increasingly common. Examples of such applications include...
Apress, 2024. — 478 p. — ISBN: 9798868803536, ASIN B0CYNRV8BK. This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various...
Routledge, 2023. — 181 p. — ISBN: 9781032362427. Machine Learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with Machine Learning (ML) are better placed to navigate the increasingly digital world we live in. There is a view that Machine Learning is a highly technical subject that can only be...
Independently published, 2019. — 182 p. — ISBN: 978-1-081-47400-3. Useful Python Tools eg. Anaconda, Jupiter Notebook Do want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model but you have no programming skill? Do you want to get started with learning data science? This audiobook is going to...
Boca Raton: CRC Press, 2024. — 223 p. Machine Learning (ML) has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However, heavy resources are required to process and analyze the data, which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response....
O’Reilly Media, October 2019. — 62 p. — ISBN: 9781098115470. Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate, but can also make their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers,...
McGraw-Hill Education, 2018. — 650 p. — ISBN13: 978-93-5316-025-8. This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent...
IGI Global, 2021. - 388 p. - ISBN: 9781799836247 Traditional machining has many limitations in today’s technology-driven world, which has caused industrial professionals to begin implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic algorithms has recently transformed the manufacturing...
Manning Publications, 2021. — 502 p. — ISBN: 9781617296819. The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what...
Springer, 2022. — 78 p. — (SpringerBriefs in Computer Science). — ISBN: 978-981-19-4873-2. Emerging Machine Learning (ML) techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of Software-Defined Networking (SDN). It will apply several innovative...
CRC Press, 2019. — 364 p. — ISBN13: 978-1-138-58730-4. While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and...
Wien: Springer, 2002. — 344 p. The book provides a systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution, and density estimation, and genetic programming. The book is...
Amazon.com Services LLC., 2019. — 168 p. — ISBN 1706955286, ASIN B0818Z9Z7C. Created with the beginner in mind, this powerful bundle delves into the fundamentals behind Machine Learning, from basic code and mathematical formulas to complex neural networks and ensemble modeling. Inside, you'll discover everything you need to know to get started with machine learning, and begin...
Independently published, 2024. — 107 p. Machine Learning is revolutionizing the world, and Python is the language of choice for its development. This book equips you with the essential tools - Pandas, Scikit-learn, and TensorFlow - to build and deploy intelligent applications. Written by seasoned practitioners, this book combines clear explanations with practical exercises,...
Independently published, 2024. — 107 p. Machine Learning is revolutionizing the world, and Python is the language of choice for its development. This book equips you with the essential tools - Pandas, Scikit-learn, and TensorFlow - to build and deploy intelligent applications. Written by seasoned practitioners, this book combines clear explanations with practical exercises,...
O’Reilly, 2019. — 320 p. — ISBN: 1492047546. With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for...
Springer, 2020. — 640 p. — ISBN: 3030593371. This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and...
New York: IGI Global, 2017. — 1270 p. Continuous improvements in technological applications have allowed more opportunities to develop automated systems. This not only leads to higher success in smart data analysis, but it increases the overall probability of technological progression. The Handbook of Research on Machine Learning Innovations and Trends is a key resource on the...
Springer, 2021. — 214 p. — (Lecture Notes in Mathematics 2293). — ISBN: 978-3-030-77561-2. The study of Calabi–Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to understand the underlying structure of...
Elsevier Inc., 2022. — 234 p. — ISBN: 978-0-12-818721-0. Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific...
Apress, 2021. — 300 p. — ISBN13: 978-1-4842-7031-8. Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle’s Autonomous Database cloud offering. The book covers the technologies that make up the Oracle Machine Learning (OML) platform, including OML4SQL, OML Notebooks, OML4R, and OML4Py. The book...
Routledge, 2023. — 191 p. — ISBN: 978-1-032-07277-7. Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just...
Walter de Gruyter GmbH, 2021. — 216 p. — ISBN: 978-3-11-070248-4. Machine learning (ML) is a part of computerized reasoning which comprises algorithms and artificial neural networks and displays qualities firmly connected with human insight. The book focuses on the applications of ML for sustainable development. This book provides an understanding of sustainable development and...
CRC Press, 2023. — 346 p. — eBook ISBN: 9781003187158. Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and...
Springer, 2023. — 319 p. — (Artificial Intelligence: Foundations, Theory, and Algorithms). Machine Learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it...
New York: Amazon.com Services LLC, 2020. — 37 p. Are you prepared for the inevitable AI revolution? How can you use it in your current role as a business leader? Artificial intelligence has become the center of strategic decision making for organizations. It disrupts the way industries function - from sales and marketing to finance and HR, companies are betting on AI to give...
CRC Press, 2021. — 402 p. — ISBN: 978-0-367-55418-7; ISBN: 978-0-367-55419-4; ISBN: 978-1-003-09345-9. Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text...
Birmingham: Packt Publishing, 2022. - 335 p. - ISBN: 1803249900. Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational use. Key Features Learn best practices for bringing your models to production. Explore the tools available for serving...
The Guilford Press, 2023. — 435 p. — ISBN: 978-1462552924. Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data...
IGI Global, 2024. — 308 p. — eBook ISBN: 9798369338841. Organizations worldwide grapple with the complexities of incorporating machine learning into their business models while ensuring sustainability. Decision-makers, data scientists, and business executives face the challenge of navigating this terrain to drive innovation and maintain a competitive edge. Building Business...
Birmingham: Packt Publishing, 2022. — 280 p. — ISBN: 1803230061. Learn how to leverage feature stores to make the most of your machine learning models Key Features Understand the significance of feature stores in the ML life cycle. Discover how features can be shared, discovered, and re-used. Learn to make features available for online models during inference. Feature store is...
Boca Raton: CRC Press, 2022. — 397 p. Rapid population growth coupled with the evolution of numerous diseases is a matter of concern worldwide. Due to this, the healthcare industry has emerged as an essential service sector. The generation of a large amount of healthcare data and the lack of insight from that data are significant problems in the healthcare sector. Therefore,...
Springer, 2022. - 226 p. - (Machine Learning: Foundations, Methodologies, and Applications). - ISBN: 9811970823. This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning...
Springer, 2020. — 627 p. This book gathers papers addressing state-of-the-art research in the areas of machine learning and predictive analysis, presented virtually at the Fourth International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2020), India. It covers topics such as intelligent agent and multi-agent systems in various domains,...
Packt, 2018. - 356 p. - ISBN: 9781788998246 Code files only! A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript Key Features Solve complex computational problems in browser with JavaScript Teach your browser how to learn from rules using the power of machine learning Understand discoveries on web interface and API in...
CRC Press, 2022. — 442 p. — (Chapman & Hall / CRC Data Mining and Knowledge Discovery Series). — ISBN: 9781003143376. Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability...
New York: Apress, 2018. — 355 p. Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine...
2nd.ed. - The MIT Press, 2020. - 856 p. - ISBN: 0262044692 The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications...
Packt Publishing, 2022. — 395 p. — ISBN: 1800566018. Build predictive models using large data volumes and deploy them to production using cutting-edge techniques. Key Features Build highly accurate state-of-the-art machine learning models against large-scale data. Deploy models for batch, real-time, and streaming data in a wide variety of target production systems. Explore all...
Packt Publishing, 2022. — 382 p. Key Features: Understand the need for high-performance computing (HPC). Build, train, and deploy large ML models with billions of parameters using Amazon SageMaker. Learn best practices and architectures for implementing ML at scale using HPC. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then...
Packt Publishing Ltd, 2025. — 485 p. Apply supervised and unsupervised machine learning algorithms using C++ libraries, such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib using real-world examples and datasets. Key Features. Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries. Implement practical machine...
Taylor & Francis Group, LLC, 2021. — 176 p. — ISBN: 978-0-367-27732-1. AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify...
Birmingham: Packt Publishing, 2022. — 257 p. — ISBN: 180324836X. Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming. Key Features Work on streaming use cases that are not taught in most data science courses. Gain experience with state-of-the-art tools for streaming data. Mitigate various challenges...
New York: Springer, 2022. — 260 p. This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects – one focuses on architecting a choice process to lead users on a certain choice path while the second focuses on developing machine learning models based on a choice...
Leanpub, 2022. — 352 p. This book provides a guided tour along the wide range of ML methods that have proven useful in the process industry. Step-by-step instructions, supported with real process datasets, show how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, soft sensing, and process control. This book provides an...
Boca Raton: CRC Press, 2022. — 232 p. Several approaches are being defined for statistics and Machine Learning. These approaches are used for the identification of the process of the system and the models created from the system's perceived data, assisting scientists in the generation or refinement of current models. Machine Learning (ML) is being studied extensively in...
Wiley-Scrivener, 2023. — 239 p. This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design. Artificial Intelligence (AI) and Machine Learning (ML)...
CRC Press, 2022. — 199 p. — ISBN: 978-0-367-62256-5. Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry. It has enabled us to share resources among multiple users through virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed...
Springer, 2021. — 341 p. — (Studies in Big Data). — ISBN: 978-981-15-9491-5. This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in...
New York: Wiley-Scrivener, 2022. — 416 p. The book focuses on the way that human beings and computers interact to ever-increasing levels of both complexity and simplicity. Assuming very little knowledge, the book provides content on theory, cognition, design, evaluation, and user diversity. It aims to explain the underlying causes of the cognitive, social and organizational...
Artech House, 2025. — 265 p. — ISBN-13: 978-1-68569-033-5. Radio Frequency Machine Learning: A Practical Deep Learning Perspective goes beyond general introductions to deep learning, offering a focused exploration of how modern deep learning techniques can be applied directly to radio frequency (RF) challenges. It covers a wide range of applications, including classification...
Springer Vieweg, 2023. — 132 p. — ISBN: 3658404418. Machine learning has become key in supporting decision-making processes across a wide array of applications, ranging from autonomous vehicles to malware detection. However, while highly accurate, these algorithms have been shown to exhibit vulnerabilities, in which they could be deceived to return preferred predictions....
O’Reilly, 2021. — 482 p. — ISBN: 1098102363. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides...
O’Reilly Media, Inc., 2021. — 481 p. — ISBN: 9781098102364. By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including classification, object detection,...
O'Relly, 2021. — 156 p. — ISBN: 978-1098115715. The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the...
O’Reilly Media, Inc., 2020. — 408 p. — ISBN: 978-1-098-11578-4. Code files only! The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of...
Packt, 2019. - 458 p. - 978-1788295864 3rd.ed. Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by...
Oxford University Press, 2021. — 333 p. — ISBN: 9780190941666. Teaches the machine learning process for business students and professionals using automated machine learning, a new development in data science that requires only a few weeks to learn instead of years of training Though the concept of computers learning to solve a problem may still conjure thoughts of futuristic...
Packt Publishing, 2021. — 249 p. — ISBN: 978-1-80056-079-6. Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key Features Explore machine learning workflows for stating ML problems in a concise and clear manner using MLflow Use MLflow to iteratively develop an ML model and manage it Discover and work with...
O’Reilly Media, Inc., 2023. — 317 p. — ISBN-13: 978-1-098-10665-2. With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph,...
Springer, 2022. — 109 p. — (Wireless Networks). — ISBN: 978-3-031-16374-6. This book demonstrates the optimal adversarial attacks against several important signal-processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing...
New York: Springer, 2022. — 283 p. In the big data era, increasing information can be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, etc. These multiple types of data...
CRC Press, Taylor & Francis Group, LLC., 2022. — 404 p. — ISBN: 9780367550547. Spatial predictive modeling (SPM) is an emerging discipline in applied sciences, playing a key role in the generation of spatial predictions in various disciplines. SPM refers to preparing relevant data, developing optimal predictive models based on point data, and then generating spatial...
Springer, 2023. — 302 p. — ISBN: 978-3-031-35050-4. This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of...
Springer, 2022. — 274 p. Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning...
3rd edition. — Packt Publishing, 2020. — 527 p. — ISBN 9781800209718. A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key Features Dive into machine learning algorithms to solve the complex challenges faced by data scientists...
Bentham Books, 2022. — 240 p. — ISBN 978-981-5079-19-7. This book is a quick review of Machine Learning methods for engineering applications. It provides an introduction to the principles of Machine Learning and common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field. Next, it...
Scientific Books, 2022. — 257 p. In this book, supervised learning techniques related to regression will be developed. More specifically, we will go deeper into the linear models, LASSO regression, LARS LASSO regression, RIDGE Regression, Least Angle Regression, Multitask LASSO regression, Elastic Net Regression, Multi-task Elastic Net Regression, SGD Regression, Support Vector...
ITexLi, 2022. — 156 p. — ISBN: 1839684771 9781839684777 1839684739 9781839684739 183968478X 9781839684784. This book discusses the theoretical analysis of active learning and contains practical recommendations for its implementation. In the context of globalization changes in educational systems, it is important to modify approaches to the educational process and introduce...
Apress Media LLC, 2024. — 342 p. — ISBN-13 979-8-8688-0375-8. Understand how to use MLOps as an engineering discipline to help with the challenges of bringing Machine Learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate Machine Learning into their processes and products to improve their competitiveness. The...
CRC Press, 2022. — 243 p. — (Computational Intelligence in Engineering Problem-Solving). — ISBN: 978-1-032-04723-2. The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. The book discusses...
Orange Education Pvt. Ltd, 2024. — 247 p. — ISBN: 8197256373, 978-8197256370, ASIN B0D8L3Q283. Dive into the world of machine learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET. The book begins with the foundations of ML.NET and seamlessly transitions into practical guidance on installing and...
Springer, 2020. — 201 p. — ISBN: 9811586098. This book focuses on both theory and applications in the broad areas of computational intelligence and machine learning. The proceedings of the Seventh International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2019) present research papers in the areas of advanced computing, networking, and informatics. It...
CRC Press, 2022. — 447 p. — ISBN: 978-0-367-67989-7. Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent...
Packt Publishing, 2021. — 344 p. — ISBN: 978-1-80107-812-2. Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML Key Features Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML Leverage SQL syntax to train,...
First edition — Packt Publishing Ltd, Jun 2022 — 385 p. — ISBN: 978-1-80324-180-7 Machine Learning (ML) is the new black. Organizations are investing in adopting and uplifting their ML capabilities to build new products and improve customer experience. The focus of this book is on assisting organizations and teams to get business value out of ML initiatives. By implementing...
New York: Amazon.com Services LLC, 2020. — 92 p. Interested in the field of Artificial Intelligent (AI)? Then this book is for you! This book has been designed to learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of AI. With every tutorial you will develop new skills and improve your understanding of this...
Packt, 2019. — 403 p. Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services Key Features Implement data science and machine learning techniques to draw insights from real-world data Understand what IBM Cloud platform can help you to implement cognitive insights within applications Understand the role of data representation and...
IGI Global, 2022. — 305 p. — (Advances in Systems Analysis, Software Engineering, and High-Performance Computing). — ISBN: 1799883507. Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and...
New York: Morgan & Claypool, 2018. — 172 p. Covariance Matrices and Applications Data Representation by Covariance Matrices Covariance Matrices for Data Representation Statistical Interpretation The geometry of SPD Matrices Euclidean Distance Interpretations and Motivations for the Different Invariances Basic Riemannian Geometry Affine-invariant Riemannian Metric on SPD...
Independently published, 2021. — 93 p. — ASIN B0952D2T8C. The Machine Learning process is revolutionizing many sectors in engineering including manufacturing sectors such as the Friction Stir Welding Process. The implementation of machine learning algorithms in Friction Stir Welding process not only reduced the time of the experiment but also reduces the cost of the experiment....
New York: lulu.com, 2020. — 255 p. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like...
De Gruyter, 2023. — 478 p. — ISBN: 978-3-11-078597-5. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
De Gruyter, 2023. — 364 p. — ISBN: 978-3-11-078595-1. Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the...
Apress, 2021. — 192 p. — ISBN: 1484265424, 9781484265420. Get introduced to ML.NET, a new open-source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible. Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application...
Manning Publications, 2020. — 371 p. — ISBN: 978-1617296741. “Human-in-the-Loop machine learning” refers to the need for human interaction with machine learning systems to improve human performance, machine performance, or both. Most machine learning projects do not have the time or budget for human input on every data point, and so need strategies for deciding which data...
Springer, 2023. — 101 p. — (SpringerBriefs in Computer Science). This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve several challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and...
Packt Publishing, 2022. - 376 p. - ISBN: 180324710X. Learn the essential skills for building an authentic federated learning system with Python and take your machine-learning applications to the next level. Key Features Design distributed systems that can be applied to real-world federated learning applications at scale. Discover multiple aggregation schemes applicable to...
Apress, 2022. — 343 p. — ISBN: 9781484278017, 9781484278024. Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts of machine learning interpretability along with different types of explainability algorithms. You’ll begin by reviewing the theoretical aspects of machine learning...
Springer, 2022. — 390 p. — (Power Systems). — ISBN: 978-3-030-77695-4. This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction,...
Manning Publications, 2020. — 360 p. — ISBN: 978-1617295645. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core...
Independently published, 2024. — 84 p. Do you find yourself unsure of how to apply your existing knowledge to Python? If you are a beginner programmer who wants to learn Python Machine Learning, this book is for you. This book will help you understand how to use Python to apply your existing skills to Machine Learning problems. Machine Learning is a fascinating and powerful...
Manning, 2023. — 219 p. Causal AI teaches you how to build machine learning and deep learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You’ll learn...
1st Edition: arXiv:1909.11512v1, 2019. — 156 p. Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. First, we discuss synthetic datasets for basic computer...
Independently published, Norton D., 2023. — 196 p. Embark on a transformative journey into the realm of Artificial Intelligence with Machine Learning Mastery. This meticulously crafted hardcover, audiobook, paperback, and e-book compilation is your key to unlocking the profound capabilities of Machine Learning. From foundational theories to real-world applications, this guide...
Springer, 2021. — 170 p. — ISBN: 978-3-030-74041-2. This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing...
Springer, 2021. — 769 p. — ISBN: 978-3-030-70541-1. This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and...
Springer, 2022. — 54 p. — ISBN: 978-3-031-15892-6. This SpringerBrief presents the underlying principles of Machine Learning (ML) and how to deploy various Deep Learning (DL) tools and techniques to tackle and solve certain challenges facing the cybersecurity industry. By implementing innovative Deep Learning solutions, cybersecurity researchers, students, and practitioners can...
Manning Publications, 2021. — 250 p. — ISBN: 1617297763, 9781617297762. Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Cloud Native Machine Learning helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to...
Independently published, 2021. — 201 p. — ISBN B08QGZJDHK. Master the world of machine learning and data science with this comprehensive beginner’s bundle. Data Science and Machine Learning are the biggest buzzwords in the business world nowadays. If you want to learn more about Machine Learning and Data Science or how to master them with Python quickly and easily – we have the...
Springer, 2022. - 143 p. - (Machine Learning: Foundations, Methodologies, and Applications). - ISBN: 9811968969. This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing....
O’Reilly Media, 2021. — 304 p. — ISBN: 1492053279, 9781492053279. Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads–a process Kubeflow makes much easier. This practical book shows data scientists,...
New York: Amazon.com Services LLC, 2020. — 76 p. Well, machine learning is becoming a widely-used word on everybody's tongue, and this is reasonable as data is everywhere, and it needs something to get use of it and unleash its hidden secrets, and since humans' mental skills cannot withstand that amount of data, it comes the need to learn machines to do that for us. So we...
CRC Press, 2024. — 133 p. Machine Learning (ML) and Deep Learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in...
Springer, 2021. — 197 p. — ISBN: 978-3-030-72068-1. This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical...
Packt, 2022. — 440 p. — ISBN: 1801072167, 9781801072168. Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building,...
Apress, 2024. — 731 p. — ISBN-13 979-8-8688-1023-7. This book is the first comprehensive guide to the intersection of data governance and Machine Learning (ML) projects. As ML applications proliferate, the quality, reliability, and ethical use of data are central to their success, which gives ML data governance unprecedented significance. However, adapting data governance...
New York: Springer, 2010. — 527 p. This book presents the first cohesive treatment of Information Theoretic Learning (ITL) algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. ITL is a framework where the conventional concepts of second order statistics (covariance, L2 distances, correlation functions) are substituted by...
O’Reilly Media, Inc., 2023. — 303 p. With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure,...
O’Reilly Media, 2023. — 271 p. — ISBN-13: 978-1-098-11722-1. Get started with Ray, the open-source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use...
Packt Publishing, 2021. — 270 p. — ISBN: 978-1800567887. Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key Features Understand parallelism and how to achieve it in Python. Learn how to use neurons, layers, and activation functions and structure an artificial neural network. Tune TPOT models...
IOP Publishing, 2023. — 234 p. This book presents Machine Learning (ML) concepts with a hands-on approach for physicists. The goal is to both educate and enable a larger part of the community with these skills. This will lead to wider applications of modern ML techniques in physics. Accessible to physical science students, the book assumes a familiarity with statistical physics...
Packt, 2023. — 242 p. Master the art of machine learning by using the one-of-a-kind Qlik platform, and take your data analytics skills to the next level. Key Features. Gain a solid understanding of machine learning concepts and learn to effectively define a problem. Explore the application of machine learning principles within the Qlik platform. Apply your knowledge of ML to...
Sunil Rao, Sameeksha Katoch, Vivek Narayanaswamy, Gowtham Muniraju, Cihan Tepedelenlioglu, Andreas Spanias, Pavan Turaga, Raja Ayyanar, Devarajan Srinivasan. — Morgan & Claypool Publishers, 2020. — 91 p. — ISBN: 978-1681739076. The efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance....
San Francisco: No Starch Press, 2024. — 263 p. — ISBN: 1718503768. Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field. If you’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things...
Springer, 2023. — 371 p. — (Adaptation, Learning, and Optimization 27). — ISBN: 978-3-031-11747-3. This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of Federated Learning (FL) as well as its connection...
Manning Publications, 2020. — 513 p. — ISBN: 978-1617296574. Machine Learning with R, tidyverse, and mlr teaches you how to gain valuable insights from your data using the powerful R programming language. In his engaging and informal style, author and R expert Hefin Ioan Rhys lays a firm foundation of ML basics and introduces you to the tidyverse, a powerful set of R tools...
Princeton University Press, 2024. — 393 p. Data Science methods and tools — including programming, data management, visualization, and Machine Learning — and their application to neuroimaging research. As neuroimaging turns toward data-intensive discovery, researchers in the field must learn to access, manage, and analyze datasets at unprecedented scales. Concerns about...
CRC Press, 2021. — 221 p. — ISBN: 9780367703868. Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This...
Springer, 2020. — 221 p. — ISBN: 9811536880. The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics...
Springer, 2022. — 222 p. — (Studies in Big Data 103). — ISBN: 978-981-16-9157-7. Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN,...
Springer, 2024. — 265 p. This volume is focused on the review of recent algorithmic and mathematical advances and the development of new research directions for Mathematical Model Approximations via RAMSES (Reduced order models, Approximation theory, Machine learning, Surrogates, Emulators, Simulators) in the setting of parametrized partial differential equations also with...
Springer, 2022. — 128 p. This book discusses Versatile Video Coding (VVC), the ISO and ITU state-of-the-art video coding standards. VVC reaches a compression efficiency significantly higher than its predecessor standard (HEVC) and it has a high versatility for efficient use in a broad range of applications and different types of video content, including Ultra-High Definition...
Packt Publishing, 2019. — 327 p. — ISBN: 978-1-78913-660-9. Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of...
Springer, 2021. — 164 p. This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision-making. Thus, we have bestowed a comprehensive study on this topic that explores multi-dimensional contexts...
Satchidananda Dehuri, Bhabani Shankar Prasad Mishra, Pradeep Kumar Mallick, Sung-Bae Cho, Margarita N. Favorskaya. — Springer, 2020. — 258 p. — (Learning and Analytics in Intelligent Systems 10). This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several...
SAS Institute, 2022. — 364 p. Discover how to build decision trees using SAS Viya! Tree-Based Machine Learning Methods in SAS Viya cover everything from using a single tree to more advanced bagging and boosting ensemble methods. The book includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forests, and...
University of Würzburg, Germany, Univ.-Prof. Dr. rer. nat. Ingo Scholtes, 2022, 76 p. Introducing the Chair of Informatics XV. Research interests; Teaching portfolio; Machine Learning for Graph-Structured Data. what is ML; supervised vs unsupervised; ML for euclidean data; learning in graph-structured data; geometric ML; Interdisciplinary Applications of Graph Learning. Course...
Oxford University Press, 2022. — 400 p. — ISBN: 9780192845290. The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders...
Packt Publishing, 2020. — 490 p. — ISBN: 9781800208919, 180020891X. Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key Features - Build, train, and deploy machine learning models quickly using...
Apress, 2020. - 258 p. - ISBN: 9781484262214 Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load...
Springer, 2024. — 357 p. — (Studies in Big Data). This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data...
Packt Publishing, 2021. — 194 p. — ISBN: 978-1800567641. Create better and easy-to-use deep learning models with AutoKeras Key Features Design and implement your own custom machine learning models using the features of AutoKeras Learn how to use AutoKeras for techniques such as classification, regression, and sentiment analysis Get familiar with advanced concepts as...
IGI Global, 2020. - 700 p. - ISBN: 1522596437 As todays world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to...
Packt Publishing, 2021. — 338 p. — ISBN: 978-1800204492. Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods...
CRC Press, 2024. — 99 p. Nearly all our safety data collection and reporting systems are backward-looking: at incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where...
Springer, 2021. — 147 p. — (Terrorism, Security, and Computation). — ISBN: 3030606139, 9783030606138. This is the first study of Boko Haram that brings advanced data-driven, machine learning models to both learn models capable of predicting a wide range of attacks carried out by Boko Haram, as well as develop data-driven policies to shape Boko Haram’s behavior and reduce...
The MIT Press, 2022. — 325 p. — ISBN: 978-0262047074. Fundamental theory and practical algorithms of weakly supervised classification, emphasize an approach based on empirical risk minimization. Standard Machine Learning (ML) techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely...
Springer, 2022. — 215 p. The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows. The...
Springer, 2022. — 203 p. The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs.
Academic Press, 2021. — 324 p. — ISBN: 978-0-12-818279-6. Thinking Machines: Machine Learning and Its Hardware Implementation covers the theory and application of machine learning, neuromorphic computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning. It presents not only a summary of the...
CRC Press / Chapman & Hall, 2023. — 228 p. — (Chapman & Hall/CRC Cyber-Physical Systems). — ISBN: 9781032146416. Machine Learning, Cyber Security, and Blockchain in Smart Environment: Application and Challenges provide far-reaching insights into the recent techniques forming the backbone of smart environments, and address the vulnerabilities that give rise to the challenges in...
Wiley, 2023. — 291 p. — ISBN: 9781119898870. Data Exfiltration Threats and Prevention Techniques Comprehensive resource covering threat prevention techniques for data exfiltration and applying machine learning applications to aid in identification and prevention. Data Exfiltration Threats and Prevention Techniques provides readers the knowledge needed to prevent and protect...
Independently published, 2023. — 83 p. Machine Learning is a fascinating field that has the potential to revolutionize the way we live and work. However, it can be overwhelming for beginners to understand the complex algorithms and concepts involved. This book is designed to make Machine Learning accessible and easy-to-understand for anyone who wants to learn. The algorithms...
O’Reilly Media, 2020. — 432 p. — ISBN: 978-1-492-07305-5. Code files only! Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in...
MDPI, 2020. — 110 p. This collection of articles addresses the most modern forms of loss reserving methodology: granular models and machine learning models. New methodologies come with questions about their applicability. These questions are discussed in one article, which focuses on the relative merits of granular and machine learning models. Others illustrate applications...
FreePress, 2020. — 490 p. Learn how to trade for a living with solid risk-management strategies. Use techniques based on trader psychology for your investment in options and forex. Earn extra income, create your source of passive income, and get your financial freedom.
Springer, 2021. — 504 p. — ISBN: 9811595151. This book gathers selected papers presented at the International Conference on Machine Intelligence and Soft Computing (ICMISC 2020), held jointly by Vignan’s Institute of Information Technology, Visakhapatnam, India and VFSTR Deemed to be University, Guntur, AP, India during 03-04 September 2020. Topics covered in the book include...
First edition — Apress, 2022 — 395 p. — ISBN: 978-1-4842-8120-8 Data science and machine learning can be practiced with varying degrees of efficiency and productivity. This book focuses specifically on Python-based tools and techniques to help data scientists, beginners, and seasoned professionals alike, become highly productive at all aspects of typical data science tasks....
Leanpub, Giannis Tolios, 2022. — 107 p. A beginner-friendly introduction to Machine Learning (ML) with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of Artificial Intelligence (AI), by easily training and deploying their ML models! The field of Machine Learning has grown substantially in the past years due to...
CRC Press, 2022. — 261 p. — ISBN: 978-1-032-07159-6. Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modeling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modeling, regularized regression, support vector machine,...
Springer, 2022. — 237 p. — (Learning and Analytics in Intelligent Systems, 23). — ISBN: 978-3-030-76793-8. As the 4th Industrial Revolution is restructuring human societal organization into, the so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and...
Boca Raton: CRC Press, 2024. — 210 p. Intel oneAPI is a unified programming model and software development kit (SDK) from Intel that empowers software developers to generate high-performance applications that can run on different devices, comprising CPUs, GPUs, FPGAs, and other accelerators. It lets developers write code once and deploy it on multiple architectures, decreasing...
New York: Springer, 2020. — 256 p. This book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and engineering applications in advanced welding and cutting processes. It describes a number of advanced welding and cutting processes and then assesses the parametrical interdependencies of two entities,...
Manning, 2023. — 352 p. Discover all practical implementations of the key algorithms and models for handling unlabelled data. Full of case studies demonstrating how to apply each technique to real-world problems. The book introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine-learning models for unannotated data. You’ll...
Packt Publishing, 2020. — 311 p. — ISBN 9781839218354. Get to grips with building robust XGBoost models using Python and scikit-learn for deployment Key Features Get up and running with machine learning and understand how to boost models with XGBoost in no time Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results Discover tips and...
Birmingham: Packt Publishing, 2022. — 541 p. — ISBN: 1803241675. Explore supercharged machine learning techniques to take care of your data laundry loads. Key Features Learn how to prepare data for machine learning processes. Understand which algorithms are based on prediction objectives and the properties of the data. Explore how to interpret and evaluate the results from...
John Wiley & Sons, 2023. — 322 p. — ISBN: 9781119817482. There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning...
Elsevier, 2023. — 254 p. – ISBN: 978-0-323-96126-4. Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in Machine Learning provide new methods to tackle challenging transportation problems. This textbook is designed for...
ITexLi, 2023. — 177 p. — ISBN: 1837687161 9781837687169 1837687153 9781837687152 183768717X 9781837687176. Rapid advancements in technology have had a significant impact on teaching and learning. Globally, we have experienced an increase in innovative pedagogies and disruptive learning spaces, which have transformed teaching and learning. Tremendous changes in educational...
Boca Raton: CRC Press, 2022. — 93 p. This book provides advanced techniques for precision compensation and fault diagnosis of precision motion systems and rotating machinery. Techniques and applications through experiments and case studies for intelligent precision compensation and fault diagnosis are offered along with the introduction of machine learning and deep learning...
The Institution of Engineering and Technology, 2021. — 261 p. — (IEM Computing Series, 39). — ISBN: 978-1839530814. The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide...
Berlin: Walter de Gruyter, 2020. — 152 p. — (De Gruyter STEM). — ISBN: 3110595532. Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian learning, support vector machines, AdaBoost, compressive sensing and so on. From the preface: "With numerous books focusing on machine learning and visual...
CRC Press, 2020. — 366 p. — ISBN13: 978-1-138-60182-6. This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of...
Academic Press/Elsevier, 2023. — 404 p. — ISBN: 978-0-323-89931-4. Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solve intelligent tasks that humans can achieve, even transcending what they can achieve. Meta-Learning:...
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