A sub-field of Computer Sciences, Artificial Intelligence has a strong foundation in subjects such as mathematics, statistics, probability, etc. Therefore, any student who aspires to learn Machine Learning, Deep Learning and Data Sciences must expand their knowledge and hone their skill by reading some of the seminal works in the field. Here is a list of five such books that are available online for free. We have provided links to each of these books, too.

• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This popular textbook, published byMIT Press, is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now available for free at this link. 
• A Brief Introduction to Neural Networks by David Kriesel: Neural networks are a bio-inspired mechanism of data processing, that enables computers to learn technically similar to a brain and even generalize once solutions to enough problem instances are taught. This book provides a comprehensive overview of the subject of neural networks. David Kriesel works in the Area of Data Science and Predictive Analytics for a German IT company. Read here.
• Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong: Published by Cambridge University Press, this book motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, the aim of the authors is to provide the necessary mathematical skills to read those other books. The book is split into two parts: (a) Mathematical foundations (b) Example machine learning algorithms that use the mathematical foundations. Read here.
• Interpretable Machine Learning by Christoph Molnar: This is a ‘Guide for Making Black Box Models Explainable’. This book explains to you how to make (supervised) machine learning models interpretable. The chapters contain some mathematical formulas, but you should be able to understand the ideas behind the methods even without the formulas. Read here.
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman: Focusing on Data Mining, Inference, and Prediction, this book is a part of the Springer Series in Statistics. Vast amounts of data are being generated in many fields, and the statistician’s job is to make sense of it all: to extract important patterns and trends, and understand “what the data says.” The authors call this “learning from data.” Read here.

(Written with inputs from Jibu Elias)