In 2021, here are the top 20 most useful free e-books to learn about machine learning


Machine Learning has undeniably been one of the most prominent subjects in recent years. Machine Learning Engineer was voted one of the best workers in the United States in 2019 by a poll.

In light of this trend, we’ve compiled a list of some of the best (and free) machine learning books for anyone interested in pursuing a career in the field.

  1. ISLR

The best book for learning about machine learning theory. Also paid books are seldom superior. It includes practise material in R and is a strong introduction to math. I can’t say enough good things about this novel.

  1. Neural Networks and Deep Learning

One of the easiest and fastest introductions to Deep Learning available is this free online book. It takes just a few days to read and will teach you everything you need to know about Deep Learning.

  1. Pattern Recognition and Machine Learning

You don’t need to write much of an introduction because it’s one of the most well-known theoretical Machine Learning books.

  1. Deep Learning Book

This book is the bible of Deep Learning and serves as an introduction to Deep Learning algorithms and methods for both beginners and experts.

  1. Understanding Machine Learning: From Theory to Algorithms

This is an excellent treatise on Machine Learning theory.

  1. Seven Steps to Success: Machine Learning in Practice

This tutorial is important for non-technical product managers and non-machine learning software engineers who are new to the area. Quite well written (it’s a little old and doesn’t cover Deep Learning, but it’s still useful).

  1. Rules of Machine Learning: Best practices for Machine Learning Engineering

Do you want to know what Google feels about its Machine Learning offerings? This is an excellent guide on how to manage Machine Learning items.

  1. A Brief Introduction to Machine Learning for Engineers

Almost all Machine Learning strategies are covered in this monologue. Math is easier to grasp (for people afraid of difficult Mathematical notations).

  1. Brief Introduction to Machine Learning without Deep Learning

Almost all Machine Learning strategies are covered in this monologue. Math is easier to grasp (for people afraid of difficult Mathematical notations).

  1. Introductory Machine Learning notes

Machine Learning for Total Beginners is a reference for those who are new to the field.

  1. Foundations of Machine Learning
    A thorough explanation of the mathematical principles of Machine Learning.
  2. An Introduction to Variable and Feature Selection

In conventional machine learning algorithms, feature engineering and variable selection are perhaps the most significant human inputs. (This isn’t as important in Deep Learning methods, but Deep Learning doesn’t solve everything.) This tutorial introduces you to various function engineering techniques.

  1. AutoML Book – Frank Hutter, Lars Kotthoff, Joaquin Vanschoren

Traditional Machine Learning has recently been reduced to running AutoML models (h2o, auto sklearn, or tpot, our favourite at ParallelDots) once feature engineering is complete. (In fact, there are a few automated non-domain specific automatic feature engineering methods as well.) The methods used in AutoML are covered in this book.

  1. Deep Learning with Pytorch

A free book that teaches you how to use PyTorch to learn Deep Learning. PyTorch is ParallelDots’ favourite Deep Learning library, and we recommend it to anyone doing applied Deep Learning research or production.

  1. Dive Into Deep Learning
    Another comprehensive book on Deep Learning teaches Deep Learning using Amazon’s MXNet library.
  2. Keras Github notebooks

The Keras Library is led by Francois Chollet. His book “Deep Learning in Python,” which teaches Keras Deep Learning, is highly regarded. The book is not free, but it is a good resource since all of its code is available on Github in the form of notebooks (forming a book with Deep Learning examples). It was a great resource for me when I was studying Keras a few years ago.

  1. Model-based Machine Learning
    This is a fantastic resource for Bayesian Machine Learning. Since the book teaches with Microsoft’s Infer.Net library, you will need to install IronPython to read/implement the examples.
  2. Bayesian Models for Machine Learning

Another book that covers different Bayesian Machine Learning methods.

  1. Eisenstein NLP notes

The most popular application of Machine Learning is Natural Language Processing. This collection of notes from a GATech class gives a great overview of how Machine Learning is used to interpret human language.

  1. Reinforcement Learning – Sutton and Barto

Reinforcement Learning’s holy grail. This book is essential reading for those interested in Reinforcement Learning.

  1. Gaussian Processes for Machine Learning
    Are you going to a Machine Learning work interview? These questions can be useful in determining a strategy for solving problems involving Machine Learning systems.
  2. Machine Learning Interviews Machine Learning Systems Design Chip Huyen

This book covers the aspects of Machine Learning that deal with statistical algorithms and numerical methods for solving problems, such as factorization models, dictionary learning, and Gaussian Models.

  1. Algorithmic Aspects of Machine Learning

Machine Learning is not immune to the debate, with causality making inroads into Data Science fields. Although there is no comprehensive material on the topic, here is a quick tutorial that tries to clarify the main concepts of Causality for Machine Learning.

  1. Causality for Machine Learning

Have you found the blog to be helpful? To read more about the best books to help you succeed as a data scientist, visit our other blog.