Top 5 Free Machine Learning and Deep Learning eBooks Everyone should read

1. Deep Learning Book

 

This Deep Learning book is written by top professionals in the industry Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This book is one of the best books to learn the underlying maths and theory behind all the most important Machine Learning and Deep Learning algorithms. From Feed Forward networks to Auto Encoders, it has everything you need.

 

2. Dive into Deep Learning

 

This is an interactive eBook that covers Code, Maths, Exercises, and Discussions. It provides the implementation in Numpy/MXNet, PyTorch, and Tensorflow.

This book is a complete package as it covers all the things from Theory to Practical examples.

 

3. Fastbook by Fast.ai

 

This is a unique book of its own kind that is published as Jupyter notebooks that are freely available at Github. These notebooks cover an introduction to deep learning, Fastai, and PyTorch. Fastai is a layered API for deep learning.

The best way of learning from this book is via the free Deep Learning course offered by fast.ai.

This book is also available as a hard copy at Amazon.

 

4. An Introduction to Statistical Learning with Applications in R

 

This is one of the best books in learning the underlying theory of Machine Learning and Statistical Methods. It is aimed at upper-level undergraduate students, masters students, and Ph.D. students in the non-mathematical sciences.

This book has coding labs and exercises in the R language. It covers a lot of important Machine Learning and Statistical Methods. It also has a MOOC link given on the official website with almost 15 hours of videos. You can find it here.

 

5. Interpretable Machine Learning

 

This is one of the best books on Machine Learning that I recommend everyone to read. This book is also one of the best guides on how to interpret the Machine Learning Models and their predictions.

According to the preface of the book

“All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.”

Reading this book is going to help you a lot in improving your machine learning models, their outcomes, “why they are working,” “why they are not working,” and many other questions that will definitely make you a better data scientist and machine learning engineer.

Also, you can find all the code of this book on Github here.