Top Rated Machine Learning Books

These are the Top Rated Machine Learning Books on the Market Today, in a List Format. To view any that are of interest, just click on the “Thumbnail” or “Read More” and you’ll be directed to view additional details and information on each.

TinyML – by: Pete Warden, Daniel Situnayake

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size–small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow – by: Aurélien Géron

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.

Machine Learning – by: Tom M. Mitchell

This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

Data Mining – by: Ian H. Written, Eibe Frank, Mark Hall, Christopher Pal

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.

Introduction to Machine Learning with Python – by: Andreas C. Muller, Sarah Guido

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

The Hundred-Page Machine Learning Book by: Andriy Burkov

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 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field.

Natural Language Processing with Python – by: Steven Bird, Ewan Klein, Edward Loper

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you’ll learn how to write Python programs that work with large collections of unstructured text. You’ll access richly annotated datasets using a comprehensive range of linguistic data structures, and you’ll understand the main algorithms for analyzing the content and structure of written communication.

Pattern Recognition and Machine Learning – by: Christopher M. Bishop

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern….

Machine Learning for Hackers – by: Drew Conway, John Myles White

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

An Introduction to Statistical Learning – by: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with…..

Deep Learning – by: Ian Goodfellow, Yoshua Benigo, Aaron Courville

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
—Elon Musk, Co-Chair of OpenAI; Co-Founder and CEO of Tesla and SpaceX.

Machine Learning: A Probabilistic Perspective – by: Kevin Murphy

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today’s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data.

Data Science from Scratch – by: Joel Grus

To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

Machine Learning for Dummies – by: John Paul Mueller, Luca Massaron

One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021)

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…

Python Machine Learning – by: Sebastian Raschka and Vahid Mirjalili

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Purchase of the print or Kindle book includes a free eBook in the PDF format.

Key Features…

Bayesian Reasoning and Machine Learning – by: David Barber

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science….

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