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...
Springer, 2024. — 162 p. This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention...
World Scientific Publishing, 2023. — 418 p. The juxtaposition of "Machine Learning" and "pure mathematics and theoretical physics" may first appear as contradictory in terms. The rigors of proofs and derivations in the latter seem to reside in a different world from the randomness of data and statistics in the former. Yet, an often under-appreciated component of mathematical...
The MIT Press, 2023. — 1354 p. An advanced book for researchers and graduate students working in Machine Learning and statistics who want to learn about Deep Learning, Bayesian inference, generative models, and decision-making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate...
Gistrup: River Publishers, 2023. — 251 p. This book provides various insights into Machine Learning techniques in healthcare system data and its analysis. Recent technological advancements in the healthcare system represent cutting-edge innovations and global research successes in performance modeling, analysis, and applications. The extensive use of machine learning in...
Springer, 2023. — 102 p. This book is a state-of-the-art resource that robotics researchers and engineers can use to make their robots and autonomous vehicles smarter. Readers will be able to describe metareasoning, select an appropriate metareasoning approach, and synthesize metareasoning policies. Metareasoning for Robots adopts a systems engineering perspective in which...
Springer, 2023. — 254 p. This graduate-level textbook is ideally suited for lecturing on the most relevant topics of Edge Computing and its ties to Artificial Intelligence (AI) and Machine Learning (ML) approaches. The book starts from basics and gradually advances, step-by-step, to ways AI/ML concepts can help or benefit from edge computing platforms. Using practical labs,...
Springer, 2023. — 319 p. 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 also raises new safety concerns, as Machine Learning has many...
New York: Routledge, 2023. — 364 p. Data Analytics and Adaptive Learning offer new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating,...
Springer, 2023. — 189 p. This book presents recent advances in Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including: blind source separation; social network mining; image and video processing; array...
Bodenseo, 2021. — 453 p. Not only in Machine Learning but also in general life, especially business life, you will hear questions like "How accurate is your product?" or "How precise is your machine?". When people get replies like "This is the most accurate product in its field!" or "This machine has the highest imaginable precision!", they feel comforted by both answers....
Boca Raton: CRC Press, 2023. — 357 p. This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine by combining design and problem-solving skills of engineering with health sciences to advance healthcare treatment. Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques...
Boca Raton: CRC Press, 2022. — 405 p. Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees...
O’Reilly Media, Inc., 2022. — 331 p. — ISBN: 978-1-492-08525-6. • 2021-12-07: First Release 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...
Josh Starmer, 2022. — 305 p. 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 concept is clearly...
BPB Publications, 2022. — 751 p. — ISBN: 978-93-91030-42-1. Master the ML process, from pipeline development to model deployment in production. Key Features Prime focus on feature-engineering, model-exploration & optimization, dataops, ML pipeline, and scaling ML API. A step-by-step approach to cover every data science task with utmost efficiency and highest performance. Access...
Manning Publications, 2021. — 426 p. — ISBN: 978-1617296741. 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...
Leanpub, 2020. — 274 p. — ISBN: 978-1999579579. From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI...
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...
Wiley, 2015. — 360 p. — ISBN: 978-1-118-96174-2. 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 book is...
New York: 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.
O’Reilly Media, Inc., 2020. — 408 p. — ISBN: 978-1-098-11578-4. 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 experts...
Manning Publications Co., 2022. — 339 p. — ISBN: 978-1617298059. 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...
New York: Springer, 2020. — 591 p. This book covers not only foundational materials but also the most recent progress made during the past few years in the area of Machine Learning algorithms. Despite the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progress on Machine Learning...
Cambridge: Cambridge University Press, 2022. — 337 p. 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 underpin...
New York: Springer, 2022. — 343 p. This book bridges the gap between data scientists and clinicians by introducing all relevant aspects of machine learning in an accessible way, and will certainly foster new and serendipitous applications of machine learning in the clinical neurosciences. Building from the ground up by communicating the foundational knowledge and intuitions...
Singapore: World Scientific, 2021. — 321 p. 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 causality. This...
Singapore: World Scientific, 2019. — 329 p. 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 causality. This...
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...
New York: Springer, 2021. — 181 p. The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time...
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...
Packt Publishing, 2019. — 293 p. — ISBN: 978-1-78934-979-5. Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing...
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...
Packt Publishing, 2020. — 348 p. — ISBN: 978-1-83882-441-9. Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this book,...
Arcler Press, 2019. — 347 p. — ISBN: 978-1-77407-365-0. 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 with...
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...
Packt Publishing, 2020. — 455 p. — ISBN: 978-1-78995-617-7. Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In...
2nd Edition. — Packt Publishing, 2020. — 798 p. — ISBN: 978-1-83882-029-9. 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 order to implement...
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong. — Cambridge University Press, 2019. — 417 p. — ISBN13: 978-1108455145. The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate...
Springer, 2019. — 372 p. — ISBN: 978-3-030-21809-6. 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...
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...
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...
Packt Publishing, 2019. — 158 p. — ISBN: 978-1-83855-035-6. This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Machine learning is an essential part of today’s...
2nd Edition. — Packt Publishing, 2018. — 514 p. — ISBN 978-1-78934-799-9. Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Machine learning has gained tremendous popularity for its powerful and fast...
Packt Publishing, 2018. — 576 p. — ISBN 978-1-78862-111-3. Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models Explore and master the most important algorithms for solving complex machine learning problems. Machine learning is a subset of AI that aims to make modern-day computer systems smarter and...
Packt Publishing, 2019. — 356 p. — ISBN: 978-1-78913-111-6. Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will...
2nd Edition. — O’Reilly Media, Inc., 2019. — 856 p. - ISBN: 978-1-492-03264-9. 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. This practical book shows...
Packt Publishing, 2019. — 284 p. — ISBN: 978-1-78961-285-1. Combine popular machine learning techniques to create ensemble models using Python Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to...
Packt Publishing, 2019. — 199 p. — ISBN: 978-1-78934-580-3. Leverage the power of reward-based training for your deep learning models with Python Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to...
Packt Publishing, 2019. — 285 p. — ISBN: 978-1-83882-173-9. Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to...
Packt Publishing, 2019. — 482 p. — ISBN: 978-1-78995-229-2. Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using...
2nd Edition. — Packt Publishing, 2019. — 240 p. — ISBN: 978-1-78899-459-0. Bring magic to your mobile apps using TensorFlow Lite and Core ML Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so. The book starts with...
Packt Publishing, 2020. — 419 p. — ISBN: 978-1-83921-493-6. Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into...
Packt Publishing, 2018. — 287 p. — ISBN: 978-1-78899-161-2. Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book,...
Packt Publishing, 2019. — 375 p. — ISBN: 978-1-78934-827-9. Discover the skill-sets required to implement various approaches to Machine Learning with Python Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster...
Birmingham: Packt Publishing, 2019. — 338 p. Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and...
Apress, 2018. — 362 p. — ISBN13: 978-1-4842-3563-8. 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...
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...
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.
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...
Early Release. — 2nd edition. — O’Reilly, 2019. — 510 p. — ISBN: 978-1-492-03264-9. Concepts, Tools, and Techniques to Build Intelligent Systems 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...
John Wiley & Sons, Inc., 2019. — 307 p. — ISBN: 978-1-119-54563-7. 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...
CRC Press, Taylor & Francis Group, 2014. — 457 p. — ISBN13: 978-1-4665-8333-7. 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...
Cambridge University Press, 2014. — 672 p. — ISBN: 978-0521518147. Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the...
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...
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...
Apress, 2019. — 163 p. — ISBN: 978-1-4842-4296-4. 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 machine learning:...
Ciml.info; Published by 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...
O’Reilly, 2017. — 392 p. — ISBN: 978-1-4493-6941-5. Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data...
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...
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...
Manning Publications, 2018. — 224 p. — ISBN: 978-1-6172-9333-7. 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...
Petaluma US : Roundtree Press, 2018. — 104 p. — ISBN: 978-1-944903-52-7. 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...
Springer, 2018. — 472 p. — ISBN: 978-3-030-02356-0. — (Studies in Computational Intelligence 801). The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world...
Springer, 2019. — 320 p. — ISBN: 978-3-319-89802-5. 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...
O’Reilly, 2018. — 366 p. 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 numerical data, model selection, and...
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...
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...
The MIT Press, 2017. — 272 p. — ISBN: 0262036827. If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning - programming computers to learn from data - has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing,...
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...
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...
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...
New York: Springer, 2016. — 229 p. 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 Includes exercises at the end of every chapter and MatLAB codes for all examples This is the first...
MIT Press, 2012, -1098 p. With the ever increasing amounts of data in electronic form, the need for automated methods for data analysis continues to grow. The goal of machine learning is to develop methods that can automatically detect patterns in data, and then to use the uncovered patterns to predict future data or other outcomes of interest. Machine learning is thus closely...
Publisher: Chapman and Hall/CRC; April, 2009, 457 p. A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Linear discriminants The multi-layer perceptron Radial basis functions and splines Support vector machines Learning with trees Decision by committee: ensemble learning Probability and learning Unsupervised learning Dimensionality reduction...
John Wiley & Sons, 2012. — 404 p. — ISBN: 978-0470596692. Learn the science of collecting information to make effective decisions. Everyday 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 is time-consuming and expensive. Designed...
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