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


1. Deep Learning Book


This Deep Learning e-book is written by high professionals within the business Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This e-book is likely one of the finest books to study the underlying maths and concept behind all a very powerful Machine Learning and Deep Learning algorithms. From Feed Forward networks to Auto Encoders, it has the whole lot you want.


2. Dive into Deep Learning


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

This e-book is a full bundle because it covers all of the issues from Theory to Practical examples.


3. Fastbook by


This is a novel e-book of its personal sort that’s printed as Jupyter notebooks which might be freely out there at Github. These notebooks cowl an introduction to deep studying, Fastai, and PyTorch. Fastai is a layered API for deep studying.

The finest approach of studying from this e-book is through the free Deep Learning course provided by

This e-book can also be out there as a tough copy at Amazon.


4. An Introduction to Statistical Learning with Applications in R


This is likely one of the finest books in studying the underlying concept of Machine Learning and Statistical Methods. It is aimed toward upper-level undergraduate college students, masters college students, and Ph.D. college students within the non-mathematical sciences.

This e-book has coding labs and workout routines within the R language. It covers a number of vital Machine Learning and Statistical Methods. It additionally has a MOOC hyperlink given on the official web site with nearly 15 hours of movies. You can discover it right here.


5. Interpretable Machine Learning


This is likely one of the finest books on Machine Learning that I like to recommend everybody to read. This e-book can also be probably the greatest guides on methods to interpret the Machine Learning Models and their predictions.

According to the preface of the e-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 e-book goes that will help you so much in enhancing your machine learning fashions, their outcomes, “why they are working,” “why they are not working,” and many different questions that can positively make you a greater data scientist and machine learning engineer.

Also, yow will discover all of the code of this e-book on Github right here.



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