2018. — 75 p. — ASIN B07FKZN93N. 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 beginner in learning a sound method of data analysis that uses algorithms, which learn from data and produce actionable and valuable...
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...
Springer, 2021. — 204p. — (SpringerBriefs in Applied Sciences and Technology, SpringerBriefs in Computational Intelligence). — ISBN: 978-981-33-4022-0. Network analysis has gained a lot of prominence over the past decade. This is because of a better understanding and control over learning representations of various entities like nodes, edges, subgraphs, cliques, and graphs that...
CRC Press, 2023. — 378 p. — ISBN: 978-1-003-17025-9. 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...
AI Publishing LLC, 2020. — 310 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 really driving business results. Whether it’s healthcare, e-commerce,...
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...
O’Reilly, 2018. - 366 p. - ISBN: 1491989386 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,...
O’Reilly Media, 2018. — 216 p. Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features — the numeric representations of raw data — into formats for machine-learning models. Each chapter guides you through a single data problem,...
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 edition. — The MIT Press, 2021. — 280 p. — (MIT Press essential knowledge). — ISBN: 9780262542524. A concise overview of machine learning — computer programs that learn from data — the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice...
O’Reilly, 2020. — 150 p. — ISBN: 978-1-492-04510-6. 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...
O’Reilly, 2020. — 260 p. — ISBN: 978-1-492-04510-6. 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...
Independently published, 2024. — 92 p. 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: Understanding the essence of...
Independently published, 2024. — 331 p. — ASIN: B0CW1C5S62. Many believe that a machine learning model, once trained, can act autonomously. This misconception has hindered innovation in ML/AI for far too long. In reality, ML models require integration within a comprehensive system encompassing inputs, processing, and outputs. My new book, "Applied Machine Learning: A Practical...
Apress Media LLC., 2020. — 262 p. — ISBN-13 (electronic): 978-1-4842-5772-2. 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...
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...
Manning Publications, 2025. — 376 p. — ISBN-13: 978-1633438750. 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,...
BPB Publications, 2024. — 392 p. — ISBN: 978-93-55519-818. Build high-impact ML/AI solutions by optimizing each step. Key Features. Build and fine-tune models for maximum performance. Practical tips to make your state-of-the-art AI/ML models. ML/AI problem-solving tips with multiple case studies to tackle real-world challenges. Description. This book approaches data science...
BPB Publications, 2023. — 353 p. — ISBN: 13: 978-9355518446. 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...
Springer, 2023. — 768 p. — (Genetic and Evolutionary Computation) — eBook ISBN: 978-981-99-3814-8. Explores various ways evolution can help improve current methods of machine learning. Presents real-world applications in medicine, robotics, science, finance, and other domains. Serves as an essential reference for those interested in evolutionary approaches to machine learning...
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....
Springer, 2023. — 327 p. — ISBN: 978-981-19-5169-. 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...
Tarkeshwar Barua, Kamal Kant Hiran, Ritesh Kumar Jain, Ruchi Doshi. — Walter de Gruyter, 2024. — 487 p. — (De Gruyter STEM)/ — ISBN: 13: 9783110697162. This book explains how to use the programming language Python to develop machine learning and deep learning tasks. It provides readers with a solid foundation in the fundamentals of machine learning algorithms and techniques....
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...
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,...
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...
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, 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...
Packt Publishing, 2017. — 382 p. — ISBN: 978-1-78398-028-4. Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. Machine Learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of...
2nd Edition. — BPB Publications, 2024. — 472 p. — ASIN: B0CLL37MFL. The second edition of “Machine Learning for Beginners” addresses key concepts and subjects in Machine Learning. The book begins with an introduction to the foundational principles of machine learning, followed by a discussion of data preprocessing. It then delves into feature extraction and feature selection,...
BPB Publications, 2020 — 262 p. — ISBN: 978-93-89845-42-6. Get familiar with various Supervised, Unsupervised, and Reinforcement learning algorithms Key Features Understand the types of Machine learning. Get familiar with different Feature extraction methods. Get an overview of how Neural Network Algorithms work. Learn how to implement Decision Trees and Random Forests. 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...
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...
CRC Press, 2020. — 488 p. — ISBN13: 978-1-138-49568-5. 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,...
Packt Publishing, 2017. — 449 p. — ISBN: 978-1785889622. 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 mastering...
2nd Edition. — John Wiley & Sons, Inc., 2020. — 418 p. — ISBN: 978‐1‐119‐56195‐8 (ebk). 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...
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...
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...
Manning. 2016. 427 p. Eng. eal-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, classification, and regression. You'll also...
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...
Andriy Burkov, 2019. — 160 p. — ISBN: 978-1-9995795-0-0. Update 2019-07-05 Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 p. He succeeds well in choosing the topics — both theory and practice — that will be useful to...
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...
First Release — O’Reilly Media, Inc, 2022 - 484 p. — ISBN: 978-1-492-08992-6. Welcome to Practical Simulations for Machine Learning! This book combines two of our favorite things: video game engines and artificial intelligence. We hope you enjoy reading it as much as we enjoyed writing it. Specifically, this book explores the use of Unity, a product that used to be called a...
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...
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. — ISBN-13: 978-150152-248-2. 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...
Amazon.com Services LLC., 2020. — 102 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...
Springer Cham, 2023. — 391 p. — (Statistics and Computing) — eBook ISBN: 978-3-031-41337-7. Presents the fundamental notions of supervised machine learning. Provides a balance between the theory and applications of machine learning using Python, R, and Stata. Fosters an understanding and awareness of machine learning methods over different software platforms. This book presents...
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...
Manning Publications, 2023. — 335 p. — ISBN: 978-1617298042. 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...
O’Reilly Media, Inc., 2024. — 307 p. — ISBN-13: 978-1-098-14654-2. As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But 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...
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, which...
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. The aim of this book is to present some of the innovative techniques in the...
Academic Press/Elsevier, 2023. — 300 p. — ISBN: 978-0-12-824020-5. Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms for the adversarial attack, defense, and verification. Sections cover the adversarial attack, assurance, and defense, mainly focusing on image classification applications which are the...
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...
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...
Amazon.com Services LLC., 2020. — 151 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,...
Independently published, 2020. — 201 p. — ISBN: 979-8611356791. Would you like to learn to use Python extracting meaningful insight from data to grow your business but you reckon it will be too complex? Or perhaps you want to know how to analyze data to solve simple domestic issues but you don’t know how to do it? Here’s the deal… As a beginner you will be probably afraid that...
Independently published, 2020. — 210 p. — ISBN: 979-8611346952. Would you like to learn how to use Python to generate machine learning models but you think it would be too difficult? Or perhaps you want to automate simple things with your computer but you don’t know how to do it? Here’s the deal… As a beginner you might think that programming is complex… Learning artificial...
O’Reilly Media, 2016. — 290 p. — ISBN: 978-1491964606. In Practical Machine Learning with H2O, author Darren Cook introduces readers to H2O, an open-source machine learning package that is gaining popularity in the data science community. This concise book will first teach readers how to install H2O, import and export data, and distinguish H2O algorithms. Readers will then...
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 services that use ML...
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...
CRC Press, 2024. — 289 p. — ISBN: 978-1-003-35010-1. 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...
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...
Packt Publishing, 2017. — 442 p. — ISBN: 9781788295758. 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 information on...
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, 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...
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...
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...
De Gruyter, 2024. — 316 p. — (De Gruyter Handbooks of Digital Transformation 1). The De Gruyter Handbook of Artificial Intelligence, Identity and Technology Studies examines the relationship of the social sciences to Artificial Intelligence, surveying the various convergences and divergences between science and technology studies on the one hand and identity transformations on...
O’Reilly, 2020. — 166 p. — ISBN: 1492072745. Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data — fake data generated from real data — so you can perform secondary analysis to do research,...
Microsoft Press, 2020. — 450 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...
2nd edition. — Boca Raton: CRC Press, 2025. — 242 p. A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of Machine Learning concepts from basic principles and illustrates every concept using examples in both Python and MatLAB, which are available on GitHub and can be run from there in Binder in a web browser. Each chapter...
New York: Addison-Wesley Professional, 2019. — 592 p. 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 absolute beginner. If you can write some Python code, this book...
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, 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...
Amazon.com Services LLC, 2020. — 120 p. — ISBN: 979-8615125096. You Are About To Start Your Journey To Understanding Machine Learning Like The Back Of Your Hand And Use It To Your Advantage! If you’ve always wanted to learn how computers are able to perform some complex things like suggesting which products to buy to different customers depending on their buying behavior and...
Springer Cham, 2023. — 267 p. — eBook ISBN: 978-3-031-39477-5. Tackles the 'why' questions of data science and deep learning. Interdisciplinary approach to model engineering. Information measurements for MLOps, Data drift, bias. This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the...
O’Reilly, 2023. — 360 p. — Second Edition. 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 models and leveraging neural...
Monograph 2018. - 442 p. This book implements many common machine learning algorithms in the equivalent R and Python. This is the 3rd edition of the book. The book covers R and Python implementations of various regression models, classification algorithms including logistic regression, KNN classification, SVM, b-splines, random forest, boosting, etc. The book goes on to cover...
BPB Publications, 2021. — 218 p. — ISBN: 978-93-90684-700. Utilize Python and IBM Watson to put real-life use cases into production. Key Features Use of popular Python packages for building Machine Learning solutions from scratch. Practice various IBM Watson Machine Learning tools for Computer Vision and Natural Language Processing applications. Expert-led best practices to put...
3rd Edition. – O’Reilly Media, 2023. – - 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 bestselling book uses concrete examples, minimal...
Early Release . 2nd Edition. — O’Reilly, 2019. — 600 p. 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 uses concrete...
2nd Edition. (3rd. Rev.) — 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...
Second updated edition. — O’Reilly, 2019. — 856 p. — ISBN: 1492032646. 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 you how. By using...
Independently published, 2019. — 613 p. — ISBN B07Y2H2M8R. 4 Books in 1! 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 and you have no programming skill? Do you know a bit of Python coding and want to learn more about how this deep learning works? This bundle is going to guide you to...
Springer, 2022. — 161 p. — ISBN: 978-3-031-14634-3. 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. — 256 p. 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 strong background...
Packt Publishing - ebooks Account, 2016. — 614 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...
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...
Morgan Kaufmann, 2018. — 569 p. — ISBN: 9780081006597. Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the...
O’Reilly, 2020. - 130 p. - ISBN ISBN 9781492050124 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 exploration, feature preparation, and model training to model serving. This guide helps data...
Morgan Kaufmann/Elsevier, 2024. — 408 p. 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 techniques – including...
BPB Publications, 2025. — 346 p. — ISBN: 978-93-65891-997. Description This book prepares you with the knowledge and skills to confidently excel in the exciting world of machine learning (ML) interviews and launch a successful career in this dynamic field. This book offers a collection of curated questions and answers to help readers understand key ML concepts, including data...
Springer, 2019. — 372 p. — ISBN: 978-3-030-21810-2 (eBook). 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...
O’Reilly Media, 2023. — 600 p. 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 benefit,...
O’Reilly Media, 2020. — 366 p. — ISBN: 1492053198. 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...
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...
Independently published, 2025. — 100 p. Unlock the Power of Machine Learning with This Beginner-Friendly Guide! Are you ready to dive into the exciting world of Machine Learning but don’t know where to start? "Machine Learning for Beginners: A Step-by-Step Guide with Code Examples" is your perfect starting point! This book is designed for absolute beginners who are eager to...
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...
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...
CRC Press, 2023. — 336 p. — ISBN: 978-1003187158. 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...
Apress, 2018. — 339 p. Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data...
Amazon.com Services LLC, 2020. — 51 p. — ISBN: 979-8633574968. 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...
Sonar Publishing, 2023. — 222 p. — ISBN-13: 979-8867285340. "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...
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...
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,...
Packt Publishing, 2019. — 563 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, 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...
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...
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...
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, 2018. — 503 p. — ISBN: 178934641X 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...
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...
Independently published, 2022. — 564 p. The goal of artificial intelligence is to program computer systems to make use of example information or experience to address an offered trouble. Numerous successful applications of machine learning exist already, consisting of systems that evaluate past sales data to forecast consumer habits, enhance robotic habits so that a task can be...
Springer Singapore, 2023. — 329 p. — (Machine Learning: Foundations, Methodologies, and Applications) — eBook ISBN: 978-981-19-7584-4. 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...
HiTeX Press; 2024. — 403 p. — ASIN: B0DL4V4L2G. "Few-Shot Machine Learning: Doing More with Less Data" is an illuminating exploration into the cutting-edge techniques that enable machines to learn efficiently from limited data. This book delves deep into the domain of few-shot learning — a revolutionary approach that challenges the traditional dependency on vast datasets....
Packt Publishing, 2022. — 552 p. — ISBN: 9781803246802. We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly...
Springer, 2023. — 137 p. — (Synthesis Lectures on Learning, Networks, and Algorithms). — ISBN: 978-3-031-19066-7. Stochastic gradient descent (SGD) is the backbone of supervised Machine Learning training today. Classical SGD was designed to be run on a single computing node, and its error convergence concerning the number of iterations has been extensively analyzed and improved...
CRC Press, 2023. — 298 p. — ISBN: 978-1003002611. 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...
Packt Publishing, 2016. — 258 p. — ISBN: 978-1-78439-658-9. Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning...
Packt Publishing, 2018. — 367 p. — ISBN: 978-1-78899-824-6. A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added...
O’Reilly Media, Inc., 2023. — 250 p. Whether based on academic theories or discovered empirically by humans and machines, 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 probability theory. Unlike...
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...
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...
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...
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 learning...
Boston: The MIT Press, 2015. - 624 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...
Manning Publications, 2024. — 504 p. — ISBN-13: 978-1633438835. Solve design, planning, and control problems using modern AI techniques. Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization...
O’Reilly Media, 2017. — 237 p. — ISBN: 978-1-491-92413-6. 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...
O’Reilly Media, 2017. — 99 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...
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...
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...
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...
CRC Press, 2024. — 362 p. — ISBN: 978-1-032-47162-4, 978-1-032-47163-1, 978-1-003-38485-4. 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...
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...
Jamba Academy, 2023. — 623 p. Are you ready to dive into the world of Python Machine Learning? Look no further! "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is the perfect guide for you. Written by experienced data scientist, Rajender Kumar, this book takes you on a journey through the basics of Machine Learning and the powerful Scikit-learn library. Key...
O’Reilly Media, 2021. — (Early Release). — ISBN: 978-1-492-09079-3. Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde–Google Developer Expert in machine learningand the web–provides a...
O’Reilly Media, Inc., 2021. — 482 p. — ISBN: 9781098102364. 2021-07-21: First Release 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...
O’Reilly, 2020. — 408 p. — ISBN: 9781098115784. 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 into...
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...
No Starch Press, 2021. — 273 p. — ISBN: 9781718500563. 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 intelligence....
Manning Publications, 2023. — 362 p. — (Final Release). — ISBN: 978-1617299520. 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. Evolutionary Deep Learning is a guide to improving...
3rd ed. — Packt, 2019. — 458 p. — ISBN: 1788295862. 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 3.5 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...
O’Reilly Media, 2020. — 232 p. — ISBN: 978-1-492-07277-5. Your company creates terabytes and even petabytes of data, but are you actually putting it to work? The majority of enterprises stumble on their way to becoming data-driven. Machine learning promises to reverse that trend, and early adopters are already seeing the benefits with increased ROI. In this report, author Alice...
CRC Press, 2019. — 350 p. - ISBN: 0815356226 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 revolution and...
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...
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...
O’Reilly Media, Inc., 2023. — 290 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,...
John Wiley & Sons, Inc., 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...
Addison-Wesley Professional, 2025. — 224 p. — (Addison-Wesley Data & Analytics Series). — ISBN-13: 978-0-13-834074-2. An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris...
Springer, 2024. — 530 p. — ISBN: 978-981-99-3916-9. This book provides a comprehensive and systematic introduction to the principal Machine Learning methods, covering both supervised and unsupervised learning methods. It discusses essential classification and regression methods in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy...
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...
Springer, 2022. — 288 p. — ISBN: 978-981-16-9839-2, 978-981-16-9840-8. 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...
Scientific Books, 2024. — 126 p. In this book, we will develop Machine Learning techniques related to non-linear regression. More specifically, we will go deeper into non-linear multiple regression models with all their identification, estimation, and diagnosis problems. Special emphasis is placed on generalized linear models and all types of derived non-linear models: Logit...
Springer, 2023. — 131 p. — (Springer Tracts in Advanced Robotics 156). — ISBN: 978-3-031-37831-7. One important robotics problem is “How can one program a robot to perform a task”? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book...
Dr. Wasim Akram Mandal. — 2025. — 76 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:...
CRC Press. — 2025. — 361p. — (Chapman & Hall/CRC Artificial Intelligence and Robotics Series). — ISBN: 978-1-032-87013-7. 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...
Wiley, 2019. — 236 p. — ISBN10: 1119548217, 13 978-1119548218. 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. The rapidly evolving field of artificial...
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...
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...
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,...
No Starch Press, 2024. — 272 p. — ISBN-13: 978-1718502109. Learn to expertly apply a range of machine-learning methods to real data with this practical guide. Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math. As you work through the...
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)....
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, 2024. — 259 p. — ISBN: 978-3-031-56430-7. This book is for individuals with a scientific background who aspire to apply Machine Learning (ML) within various natural science disciplines — such as physics, chemistry, biology, medicine, psychology, and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous...
Petaluma US : Roundtree Press, 2018. — 104 p. — ISBN: 978-1-944903-64-0. 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...
Sybex, 2019. — 528 p. — ISBN: 1119556716. 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...
Wiley & Sons, Inc., 2020. — 336 p. — ISBN: 978-1-119-60290-3 (ebk). 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...
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....
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...
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...
Amazon Digital Services LLC, 2019. — 46 p. Order Python Machine Learning: The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science, NumPy, Scikit Learn, Pandas and Tensorflow now to learn all the basic concepts you need to know about machine learning and Python. The purpose of...
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...
2nd Edition. — John Wiley & Sons, 2021. — 464 p. — (For Dummies). 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...
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...
O’Reilly, 2017. — 388 p. — ISBN: 978-1-491-91721-3. Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Introduction to Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the...
O’Reilly Media, 2022. — 147 p. — ISBN: 978-1-098-11913-3. Most intermediate-level machine learning books usually focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance and the need to be able to explain why and how your ML model makes the predictions that it does. This practical guide brings...
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...
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...
BPB Publications, 2020. — 338 p. — ISBN: 978-93-89845-365. An easy-to-understand guide to learn practical Machine Learning techniques with Mathematical foundations This book will be ideal for working professionals who want to learn Machine Learning from scratch. The first chapter will be an introductory chapter to make readers comfortable with the idea of Machine Learning 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...
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...
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....
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...
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...
O’Reilly Media, 2022. — 563 p. — ISBN: 978-1-098-11292-9. Healthcare is the next frontier for data science. Using the latest in machine learning, deep learning, and natural language processing, you'll be able to solve healthcare's most pressing problems: reducing the cost of care, ensuring patients get the best treatment, and increasing accessibility for the underserved. But...
Heidelberg: O’Reilly, 2018. — 183 S. — ISBN: 978-3-96009-052-6. Language: German Machine Learning erreicht beinahe alle Bereiche der Technik und der Gesellschaft. In diesem Buch bekommen Sie die schnellstmögliche Einführung in das äußerst umfangreiche Themengebiet des Machine Learning und der statistischen Datenanalyse. Dabei werden alle wesentlichen Themen abgedeckt und mit...
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...
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...
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...
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...
O’Reilly Media, 2018. — 209 p. — ISBN10: 149199584X, 13 978-1491995846. EPUB (True/HQ) Deep learning doesn't have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook,...
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...
Apress, 2017. — 382 p. — ISBN10: 1484222490, 13 978-1484222492 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...
BPB Publications, 2019 — 280 p. — ISBN: 978-93-88511-13-1. 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. Machine learning applications range 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. — ISBN-13 (electronic): 978-1-4842-6094-4. 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...
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...
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...
O’Reilly Media, 2021-12-10. — 362 p. — ISBN-13: 978-1-492-03564-0. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world’s data is unlabeled, conventional supervised learning cannot be applied;...
O’Reilly Media, 2020. — 358 p. — ISBN13: 978-3-96088-877-2. Ein Großteil der weltweit verfügbaren Daten ist ungelabelt. Auf diese nicht klassifizierten Daten lassen sich die Techniken des Supervised Learning, die im Machine Learning viel genutzt werden, nicht anwenden. Dagegen kann das Unsupervised Learning - auch unüberwachtes Lernen genannt - für ungelabelte Datensätze...
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,...
Birkhäuser Cham, 2022. — 152 p. — ISBN: 978-3-030-98316-1. 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...
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...
Final Release: O’Reilly Media, 2023. — 191 p. 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. Using Spark as...
CRC Press, 2024. — 226 p. — ISBN: 978-1-032-47841-8, 978-1-032-48399-3, 978-1-003-38884-5. 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...
Apress Media LLC, 2024. — 984 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...
O’Reilly Media, 2022. — 425 p. — ISBN: 978-1-492-09805-8. While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest,...
O’Reilly Media, 2022 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, more robust, less...
O’Reilly Media, Inc., 2023. — 376 p. — ISBN: 978-1-098-12027-6. 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...
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...
Springer, 2022. — 464 p. — ISBN: 978-3-030-96756-7. 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 networks. Most importantly, this book helps readers to understand the concepts of ML...
Abdul Rahman, Christopher Redino, Sachin Shetty, Dhruv Nandakumar, Tyler Cody, Dan Radke. — Wiley-IEEE Press, 2025. — 288 p. — ISBN-13: 978-1394206452. A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration...
3rd edition. — Packt Publishing, 2019. — 771 p. — ISBN: 9781789955750. Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features The third edition of the best-selling, widely acclaimed Python machine learning book. Clear and intuitive explanations take you deep into the theory and practice...
No Starch Press, 2024. — 264 p. — ISBN-13: 978-1-7185-0377-9. 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 fast and...
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...
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...
Manning Publications, 2020. — 537 p. — ISBN: 1617296570. Final version! 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...
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...
Lioncrest Publishing, 2021. — 222 p. — ISBN: 978-1-5445-1882-4. How can you successfully deploy AI? When AI works, it’s nothing short of brilliant, helping companies make or save tremendous amounts of money while delighting customers on an unprecedented scale. When it fails, the results can be devastating. Most AI models never make it out of testing, but those failures aren’t...
New York: Amazon.com Services LLC, 2021. — 412 p. — ISBN B08SCQ2Y75. 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....
Wiley, 2022. — 464 p. — (Wiley Finance Series). — ISBN: 9781119791386 State-of-the-art algorithmic deep learning and tensoring techniques for financial institutions The computational demand of risk calculations in financial institutions has ballooned and shows no sign of stopping. It is no longer viable to simply add more computing power to deal with this increased demand. The...
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, 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...
Business Expert Press, LLC, 2025. — 264 p. — ISBN-13 978-1-63742-749-1. EPUB (true) Machine Learning Fundamentals provides a comprehensive overview of Data Science, emphasizing Machine Learning (ML). This book covers ML fundamentals, processes, and applications that are used as industry standards. Both supervised and unsupervised learning ML models are discussed. Topics include...
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...
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.
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....
O’Reilly Media, Inc., 2024. — 329 p. — ISBN: 978-1-492-09452-4. 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...
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...
CRC Press, 2025. — 222 p. — (Data Science Series). Classification problems are common in business, medicine, science, engineering, and other sectors of the economy. Data scientists and Machine Learning professionals solve these problems through the use of classifiers. Choosing one of these data-driven classification algorithms for a given problem is a challenging task. An...
Orange Education Pvt. Ltd, 2024. — 358 p. — ISBN: 978-8197223945. Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn. Book Description “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...
2nd Edition. — Galit Shmueli, Mia L. Stephens, Muralidhara Anandamurthy, Nitin R. Patel, Peter C. Bruce. — Wiley, 2023. — 611 p. — ISBN: 978-1119903833. MACHINE LEARNING FOR BUSINESS ANALYTICS 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...
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...
2nd Edition. — Balige Publishing, 2023. — 585 p. Thyroid disease is a prevalent condition that affects the thyroid gland, leading to various health issues. In this session of the Data Science Crash Course, we will explore the classification and prediction of thyroid disease using Machine Learning and Deep Learning techniques, all implemented with the power of Python and a...
2nd Edition. — Balige Publishing, 2023. — 525 p. In this project, we will perform an analysis and prediction task on stroke data using Machine Learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about various factors related to...
2nd Edition. — Balige Publishing, 2023. — 355 p. Amazon is an American multinational technology company that is known for its e-commerce, cloud computing, digital streaming, and artificial intelligence services. It was founded by Jeff Bezos in 1994 and is headquartered in Seattle, Washington. Amazon's primary business is its online marketplace, where it offers a wide range of...
BALIGE Publishing, 2022. — 533 p. Welcome to “Machine Learning for Concrete Compressive Strength Analysis and Prediction with Python.” In this book, we will explore the fascinating field of applying machine learning techniques to analyze and predict the compressive strength of concrete. First, we will dive into the dataset, which includes various features related to concrete...
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...
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. - 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...
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...
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...
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...
Manning Publications, 2018. — 256 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 ap. Foreword by Sean Owen, Director of Data Science, Cloudera If you’re building machine learning models to...
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...
2nd edition. — CRC Press, 2023. — 866 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — 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...
O’Reilly Media, Inc., 2023. — 325 p. — ISBN-13: 978-1-098-14682-5. 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...
The MIT Press, 2022. — 325 p. — ISBN: 978-0262047074. Fundamental theory and practical algorithms of weakly supervised classification, emphasizing 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...
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
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....
Emerald Publishing, 2021. — 479 p. — ISBN: 978-1-80043-882-8. Machine Learning and Artificial Intelligence in Marketing and Sales explore 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...
O’Reilly Media, Inc., 2024. — 300 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...
Manning Publications, 2024. — 375 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...
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. 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,...
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...
W. W. Norton & Company, 2024. — 176 p. — (A Norton Short). In the industrial age, automation came for the shoemaker and the seamstress. Today, it has come for the writer, physician, programmer, and attorney. Literary Theory for Robots reveals the hidden history of modern machine intelligence, taking readers on a spellbinding journey from medieval Arabic philosophy to visions of...
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...
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...
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...
3rd edition. — Scatterplot Press, 2021. — 191 p. — ISBN B08RWBSKQB. Featured by Tableau as the first of “7 Books About Machine Learning for Beginners.” Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add ‘Machine Learning’ to your LinkedIn profile? Well, hold on there… Before you embark on your journey, there are some high-level theory and...
2nd Edition. — Academic Press, 2020. — 1160 p. — ISBN: 9780128188033. Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares, and maximum likelihood methods,...
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...
Packt Publishing, 2017. — 570 p. — ISBN: 9781785280511. Building Machine Learning applications with R. 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...
CRC Press, 2025. — 264 p. — ISBN: 978-1-003-42590-8. 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....
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...
AI Publishing LLC, 2020. — 302 p. — ISBN: 978-1-7347901-5-3. 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 really driving business results. Whether it’s healthcare,...
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...
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.
Leanpub, 2020. — 295 p.— ISBN B084FXKCS8. This version was published on 2020-07-13 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...
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...
Reactive Publishing, 2024. — 497 p. — (Python Libraries for Finance Book 9). — ASIN: B0D66BR1B1. "Machine Learning: Scikit-Learn for Finance" bridges the gap between the complex world of machine learning and practical financial applications. With a focus on hands-on examples and real-world scenarios, this book is designed to equip readers with the skills to implement...
Packt, 2023. — 905 p. Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms, and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working...
Independently Published, 2023-10-16. — 132 p. — ASIN: B0CL7M4LLZ. "Machine Learning Concepts from A to Z: A Comprehensive Guide with Code". Are you eager to unlock the potential of machine learning, from its fundamental principles to practical implementation? Look no further. "Machine Learning Concepts from A to Z" is your all-encompassing, go-to guide for understanding and...
John Wiley & Sons Inc., 2024. — 512 p. — ISBN: 978-1394220625. Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries. Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending...
Manning Publications, 2023. — 374 p. Discover all practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems. In Unsupervised Learning with Generative AI you’ll learn: Fundamental building blocks and concepts of Machine Learning and unsupervised learning. Data...
Apress Media LLC., 2020. — 392 p. — ISBN-13 (electronic): 978-1-4842-6156-9. 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...
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...
Independently published, 2019. — 129 p. — ISBN: 978-1075281518. Do you want to learn about machine learning? Are you feeling left out in the rat race where everyone is brushing up on their knowledge in the fields of AI and machine learning? Are you on a time crunch and don’t have enough time or resources to take a formal course on machine learning? If the answer to any of the...
Packt, 2022. — 542 p. — ISBN: 1803241675, 9781803241678. 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 machine...
John Wiley & Sons, 2023. — 823 p. — ISBN: 9781119817505. 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...
Johns Hopkins University Press, 2024. — 280 p. — ISBN: 978-1-4214-4923-4. How AI is revolutionizing the future of learning and how educators can adapt to this new era of human thinking. Artificial Intelligence (AI) is revolutionizing the way we learn, work, and think. Its integration into classrooms and workplaces is already underway, impacting and challenging ideas about...
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...
Independently Published, 2019. — 98 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...
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...
Wiley-IEEE, 2023. — 283 p. Extensive Resources for Understanding Key Tasks of Network and Security Management. AI and Machine Learning for Network and Security Management cover a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly...
Wiley-IEEE Press, 2024. — 275 p. — ISBN: 978-1-394-18852-9. Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively;...
Springer Singapore, 2023. — 133 p. — eBook ISBN: 978-981-99-7657-7. Presents state-of-the-art dirty data processing techniques for use in data pre-processing This opens promising avenues for the further study of dirty data processing Offers valuable take-away suggestions on dirty data processing for machine learning tasks In both the database and machine learning communities,...
Springer International Publishing AG, 2018. — 482 p. — (Human-Computer Interaction Series). — eBook ISBN: 978-3-319-90403-0. With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase in data volumes, and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of the...
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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