The field of artificial intelligence (AI) and machine learning has become increasingly popular in recent years, offering the potential to revolutionize how we interact with technology. To get up to speed on this fast-moving area, readers can benefit from reading books that offer a comprehensive overview of AI and machine learning fundamentals. This article provides an overview of some of the best AI & Machine Learning books available today, helping readers stay abreast of cutting-edge trends while mastering important concepts like Scikit-Learn, Keras, TensorFlow and mathematical foundations for successful ML models.
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Published in 2019
Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow is an excellent guide for those looking to gain a deeper understanding of machine learning. Written by experienced consultant and trainer Aurélien Géron, this book provides readers with all the necessary tools to implement their own data-learning programs using two production Python frameworks – Scikit Learn and Tensor Flow. Starting from basic linear regression techniques through more advanced deep neural networks, it contains exercises in each chapter so that readers can practice what they have learned. This comprehensive book covers everything from exploring the machine learning landscape to training models such as support vector machines and random forests, diving into convolutional nets and reinforcement learning; making it perfect for any level learner who has some programming experience.
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The StatQuest Illustrated Guide To Machine Learning
Published in 2022
The StatQuest Illustrated Guide to Machine Learning by Josh Starmer is an engaging and straightforward book that provides readers with a comprehensive overview of the complex principles behind machine learning. It begins at square one, introducing core concepts in clear language before building on those ideas step-by-step until even more advanced topics such as self-driving cars and facial recognition can be understood. The author’s illustrations are especially helpful for visual learners looking to gain further insight into these subjects. Accompanying this wealth of information is an easy-to-follow format which makes it accessible for any skill level from beginner to expert alike. An extensive appendix offers additional reference material that will prove useful when revisiting or reviewing key topics covered within the main text itself. Overall, anyone interested in learning about this rapidly expanding field should consider reading “The StatQuest Illustrated Guide To Machine Learning”.
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Mathematics for Machine Learning
Published in 2020
In Mathematics for Machine Learning, readers are presented with a comprehensive and self-contained textbook that bridges the gap between mathematical fundamentals and machine learning concepts. The text covers topics such as linear algebra, calculus, matrix decompositions, probability theory, vector calculus and optimization to provide an understanding of four core methods: linear regression analysis, principal component analysis (PCA), Gaussian mixture models and support vector machines. With its focus on developing intuition through worked examples and exercises in each chapter – along with programming tutorials available online – this book is ideal for those new to machine learning or established professionals looking to refresh their knowledge.
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The Hundred-Page Machine Learning Book
Published in 2019
This Hundred-Page Machine Learning Book, authored by Andriy Burkov is a great resource for anyone wanting to learn about the subject. It covers all major machine learning approaches from linear and logistic regression up to modern methods such as support vector machines, deep learning and random forests. The book does not assume any high level mathematical or programming knowledge so it can be accessed by almost everyone willing to invest in the time needed. With clear explanations of core concepts balanced between theory and practice and featuring Python coding examples, this book offers an invaluable starting point into the field of Machine Learning. For those wishing to extend their knowledge base even further there is also a companion wiki available with additional information on particular methods mentioned throughout the book. Karolis Urbonas Head of Data Science at Amazon praised it saying “A great introduction to machine learning from a world-class practitioner” making this publication essential reading for academics and practitioners alike..
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Introduction to Machine Learning, fourth edition
Published in 2020
Introduction to Machine Learning, fourth edition is a comprehensive textbook that provides an overview of the latest advances in the field. It covers topics such as supervised learning, Bayesian decision theory, parametric and nonparametric methods, multivariate analysis, hidden Markov models and reinforcement learning among others. The book also introduces readers to deep learning concepts like convolutional neural networks and generative adversarial networks. Furthermore it features new appendixes with background material on linear algebra and optimization plus end-of-chapter exercises to help apply concepts learnt. This revised version offers an ideal balance between theory and practice making it suitable for advanced undergraduate and graduate students as well as professionals in machine learning or data science disciplines.
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Fundamentals of Machine Learning for Predictive Data Analytics, second edition
Published in 2020
Fundamentals of Machine Learning for Predictive Data Analytics, second edition is an exceptional guide to the field of machine learning. This comprehensive introduction offers both theoretical concepts and practical applications in a concise yet thorough manner. It covers recent developments such as deep learning and unsupervised/reinforcement learning, with nontechnical explanations that are easily understandable. The two case studies provide readers with an understanding of how predictive models fit into a business context. Written by John D Kelleher, Brian Mac Namee and Aoife D’Arcy, this book can be used as either a textbook or reference manual due to its accessible writing style augmented with worked examples and mathematical material. Its balanced 360-degree view makes it ideal for novices wanting to get a firm grasp on the process – though some code examples would have been beneficial – while hardcover quality printing ensures lasting use.
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Machine Learning with PyTorch and Scikit-Learn
Published in 2022
This Machine Learning with PyTorch and Scikit-Learn book is an authoritative guide for those looking to learn or deepen their understanding of machine learning. It covers all the essential techniques in detail, from basic algorithms such as logistic regression to more recent topics like reinforcement learning and graph neural networks. The clear explanations and visualizations are complemented by real-world examples coded up using popular libraries like PyTorch Lightning, XGBoost, and others. This comprehensive reference also offers best practices on how to evaluate models and tune parameters. As a go-to resource for building practical applications with Pythonic ease, this book should be at the top of your reading list!
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Machine Learning with R
Published in 2019
Brett Lantz’s Machine Learning with R, Third Edition is a comprehensive guide to applying machine learning techniques in the real world using the R programming language. This updated and improved version for R 3.6 has been carefully crafted by an experienced practitioner of machine-learning who also serves as a DataCamp instructor. With this book, readers can gain knowledge quickly from data sets, make predictions through decision trees and support vector machines, uncover patterns with association rules, group data with k-means clustering and more. Furthermore, they will learn how to evaluate their models’ performance before improving them and connecting R to big data technologies such as Spark or H2O. An ideal read for those seeking actionable insights into their datasets!
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Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
Published in 2022
Chip Huyen’s Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications is a must-read guidebook for ML engineers, providing an in-depth look at the nuts and bolts of developing reliable, maintainable systems. Covering topics from data engineering to monitoring models’ performance in production environments, this book offers invaluable insight into building effective machine learning applications. Drawing on case studies and references as well as her own experience founding Claypot AI, Chip takes readers through each design decision step by step – including how to process training data, select features, retrain models periodically and set up a comprehensive monitoring system. With clear instructions backed by theory and practical considerations alike, Designing Machine Learning Systems provides a comprehensive framework that any reader can use when designing their own platforms. By combining technical content with business objectives it will help novices become proficient while giving experienced professionals the tips they need to advance further.
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Python Machine Learning
Published in 2019
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It serves as both an instructive tutorial and a useful reference for readers who are looking to deepen their knowledge of the latest developments in this field. This book provides clear explanations, helpful visualizations, and working examples that cover all essential machine learning techniques in detail. With code samples throughout the text, it also covers TensorFlow 2 features like its new Keras API components as well as scikit-learn additions such as reinforcement learning algorithms based on deep neural networks and Generative Adversarial Network models (GANs). Furthermore, sentiment analysis – which allows machines to classify documents – is explored within natural language processing (NLP) topics. An ideal resource for anyone teaching computers how to learn from data, Python Machine Learning offers best practices for model evaluation/hyperparameter tuning along with tools necessary for building effective predictive models.
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An Introduction to Statistical Learning: with Applications in R
Published in 2021
An Introduction to Statistical Learning with Applications in R is an accessible and comprehensive guide for understanding the field of statistical learning. Written by James, Witten, Hastie and Tibshirani, this book covers important modeling techniques such as linear regression and classification through graphical demonstrations and real-world applications. It also offers tutorials on how to implement these methods using popular open source software platform R. This second edition features new chapters on deep learning, survival analysis, multiple testing; along with expanded treatments of naïve Bayes, generalized linear models etc. What makes it a great choice for data scientists is that it provides clear explanations without requiring any prior knowledge of matrix algebra or statistics. With its color graphics refraining from overwhelming readers with technical details – An Introduction to Statistical Learning is ideal for practitioners looking to analyze complex datasets efficiently
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Introduction to Machine Learning with Python
Published in 2016
“Introduction to Machine Learning with Python: A Guide for Data Scientists”, written by Andreas Müller and Sarah Guido, is an essential guide for any data scientist looking to learn the fundamentals of machine learning. This 400-page book provides a comprehensive overview of key concept such as supervised learning, feature engineering and model evaluation. It uses practical examples in combination with scikit-learn library functions that are ideal for beginners who want to get started on building their own machine learning solutions quickly. With this book readers will gain valuable insights into advanced methods like pipelines chaining models and text processing techniques — all while remaining accessible enough even those without extensive technical knowledge can understand the material covered. In addition, it also serves as a great reference when attempting to improve your skillset related to both machine learning and general data science applications.
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Pattern Recognition and Machine Learning
Published in 2006
Pattern Recognition and Machine Learning by Chris Bishop is an authoritative guide to the field of pattern recognition from a Bayesian viewpoint. This comprehensive 700-page work presents approximate inference algorithms for situations where exact answers are not practical, utilizing graphical models for probability distributions in machine learning. Written for advanced undergraduates or beginning graduate students, no prior knowledge of these topics is assumed; however some background with multivariate calculus and basic linear algebra is necessary. The book contains an extensive 400 exercises as well as ample backing material that can be used by readers interested in self-study. It covers important statistical techniques including kernel methods and graphical models while providing geometric illustrations and intuition throughout its content. These features make it an excellent choice as a reference book or to form the basis of several advanced statistics courses.
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Artificial Intelligence: A Modern Approach, Global Edition
Published in 2021
Artificial Intelligence: A Modern Approach, Global Edition is the fourth edition of this seminal textbook on artificial intelligence. It provides an up-to-date overview of AI technology and presents concepts in a unified manner, with expanded coverage on topics such as machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing and more. This comprehensive volume also explores advanced ideas like causality and probabilistic programming alongside considerations for privacy fairness and safety. With its detailed yet accessible explanations it offers readers a great introduction to complex AI concepts – perfect for those studying or working in the field at any level from undergraduate to postgraduate. Those looking to gain practical knowledge will find that although it covers general theory well enough there are other sources better suited for translating pseudocode into actual code. All things considered Artificial Intelligence: A Modern Approach is essential reading for anyone interested in mastering this dynamic discipline.
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AI and Machine Learning for Coders
Published in 2020
Laurence Moroney’s ‘AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence’ is an ideal guidebook for computer programmers interested in transitioning into the world of artificial intelligence. It provides readers with a code-first, hands-on approach to understanding key topics such as machine learning, natural language processing (NLP) and sequence modeling. Rather than beginning with complex mathematics, it focuses on practical lessons that enable users to work directly with different coding environments like web, mobile cloud or embedded runtimes. The book covers all aspects of TensorFlow programming while also introducing ethical considerations around AI development. This comprehensive text will equip coders with the skills necessary to become successful AI specialists.
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