Machine Learning Books for Beginners

The Hundred-Page Machine Learning Book by Andriy Burkov

Best machine learning overview

In just over 100 pages, this book offers a solid introduction to machine learning in a writing style that makes AI systems easy to understand. Data professionals can use it to expand their machine-learning knowledge. Reading this book can help you prepare to speak about basic concepts in an interview. The book combines both theory and practice, illuminating significant approaches such as classical linear and logistic regression with illustrations, models, and algorithms written with Python.

Machine Learning For Absolute Beginners by Oliver Theobald

Best for absolute beginners

As the title suggests, this book delivers a basic introduction to machine learning for beginners who have zero prior knowledge of coding, math, or statistics. Theobald’s book goes step-by-step, is written in plain language, and contains visuals and explanations alongside each machine-learning algorithm. 

If you are entirely new to machine learning and data science, this is the book for you.

Machine Learning for Hackers by Drew Conway and John Myles White

Best for programmers (who enjoy practical case studies)

The authors use the term “hackers” to refer to programmers who hack together code for a specific purpose or project rather than individuals who gain unauthorized access to people’s data. This book is ideal for those with programming and coding experience but who are less familiar with the mathematics and statistics side of machine learning. 

The book uses case studies that offer practical applications of machine learning algorithms, which help to situate mathematical theories in the real world. Examples such as how to build Twitter follower recommendations keep the abstract concepts grounded. 

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron Aurelien

Best for those who know Python

If you already have experience with Python’s programming language, this book offers further guidance on understanding concepts and tools you’ll need to develop intelligent systems. Each chapter of Hands-On Machine Learning includes exercises to apply what you’ve learned.

Use this book as a resource for developing project-based technical skills that can help you land a job in machine learning.

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Best book on deep learning

This book offers a beginner-friendly introduction for those of you more interested in the deep learning aspect of machine learning. Deep Learning explores key concepts and topics of deep learning, such as linear algebra, probability and information theory, and more. 

Bonus: The book is accompanied by lectures with slides on their website and exercises on Github.

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

Best for a statistics approach

This book is an excellent tool for those who already have some knowledge of statistics. You’ll be able to understand statistical learning, and unveil the process of managing and understanding complex data sets. It covers important concepts like linear regression, tree-based models, and resample methods, and includes plenty of tutorials (using R) to apply these methods to machine learning.

Programming Collective Intelligence by Toby Segaran

Best guide for practical application

As you delve further into machine learning, with this book you’ll learn how to create algorithms for specific projects. It is a practical guide that can teach you how to customize programs that access data from websites and other applications and then collect and use that data. By the end, you’ll be able to create the algorithms that detect patterns in data, such as how to make predictions for product recommendations on social media, match singles on dating profiles, and more.

Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy

Best for an analytics approach

This is another book that provides practical applications and case studies alongside the theory behind machine learning. This book is written for those who develop on and with the internet. It takes the guesswork out of predictive data analytics, providing a comprehensive collection of algorithms and models for applying machine learning. 

Machine Learning for Humans by Vishal Maini and Samer Sabri

Best for a free resource

This final one is an e-book that is free to download [2]. It is a clear, easy-to-read guide for machine learning beginners, accompanied by code, math, and real-world examples for context. In five chapters, you’ll learn why machine learning matters, then become familiar with supervised and unsupervised learning, neural networks and deep learning, and reinforcement learning. As a bonus, it includes a list of resources for further study.