Top Machine Learning books recommended by experts

At MentorCruise, we are all about making the most out of the experience of others. As part of that, we've connected and asked dozens of experts and professionals about their favourite Machine Learning books – and here are the answers.

Table of Contents

Fundamentals of Machine Learning

Understanding the concepts of Machine Learning starts with understanding the fundamentals. On your way to mastery, it's crucial for you to understand how certain concepts were derived, and why things work like they do. Starting with these resources is the best way to do so.

Introduction to Machine Learning with Python: A Guide for Data Scientists

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…

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Machine Learning For Dummies

Machine Learning For Dummies, IBM Limited Edition, gives you insights into what machine learning is all about and how it can impact the way you can weaponize data to gain unimaginable insights. Your data is only as good as what you do with it and how you manage it.

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Deep Learning

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts …

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The Hundred-page Machine Learning Book

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 actual…

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Machine Learning with PyTorch and Scikit-Learn: Develop Machine Learning and Deep Learning Models with Python

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework.
Purchase of the print or Kindle book includes a free eBook in PDF format.

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Deep Learning Yoshua Bengio

This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

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Additional Machine Learning Reading

These books are not required for you to learn Machine Learning, but they are highly recommended for you to deepen your knowledge.

Generative Deep Learning

It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples o

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Data Mining: Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.

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AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference

Confused about AI and worried about what it means for your future and the future of the world? You’re not alone. AI is everywhere—and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works and why it often doesn’t, wh…

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The Elements of Statistical Learning

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or in…

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AI and Machine Learning for Coders

Laurence Moroney's “AI and Machine Learning for Coders” is an invaluable guide for programmers looking to transition into the exciting world of AI. This book empowers readers to implement common AI scenarios such as computer vision, NLP, and sequence modeling by taking a practical, code-first approach.

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Machine Learning for Absolute Beginners: A Plain English Introduction

Machine Learning for Absolute Beginners has been written and designed for absolute beginners. This means plain English explanations and no coding experience required. Where core algorithms are introduced, This title opens with a general introduction to machine learning from a macro level.

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Specializations and Deeper Machine Learning Knowledge

You've got your basics in order – time to move on to some advanced and specialized concepts. Machine Learning is evolving every day, these books can help you master it.

Advanced Machine Learning with Python

Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that inclu…

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Mastering Machine Learning on AWS

AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud.

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Probabilistic Machine Learning: Advanced Topics

An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies …

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This list is curated by MentorCruise and can include Amazon affiliate links. Have any other suggestions? Add here.

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