Pattern Recognition and Machine Learning (Information Science and Statistics)
This was the first Machine Learning book I touched back in college. It is an excellent textbook on statistical techniques, and it focuses on the Bayesian aspect of them. It can be a great reference on most Machine Learning algorithms but it's not ideal for beginners as it requires pretty good knowledge of Calculus and Linear Algebra.
The Elements of Statistical Learning- Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
One of the most popular machine learning books. It covers a great variety of algorithms (from supervised to unsupervised learning) and it does so by analyzing the concepts and the intuition behind them. It requires a strong statistical and mathematical background but if you want to learn how the algorithms work behind the scenes, then this is your book.
Deep Learning (Adaptive Computation and Machine Learning series)
Written by Ian Goodfellow, Yoshua Bengio and Aaron Courville ( the godfathers of modern deep learning), this book is considered the Deep Learning bible by many. Covering topics from Convolutional Networks to Autoencoders, it is a necessary buy for all AI enthusiasts. To quote Elon Musk :"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.
Python Machine Learning- Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
This book offers the practical knowledge to build and create your own Machine Learning models using the Python programmng language and the most widely used libraries in the field.
The Hundred-Page Machine Learning Book
This best seller manages to reduce all of machine learning to 100 pages. The author tried to include only the most important concepts but on the same time to help you understand complex topics, pass your AI interview and start a business. A great introductory book in the world of Machine Learning.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Concepts, Tools, and Techniques to Build Intelligent Systems
The No1 Best Seller on Artificial Intelligence on Amazon. It contains the absolute minimum of theory and it's primary focus is programming. If you want to start on coding Machine Learning Models or becoming a Data Scientist, look no more.
Machine Learning - A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
The book is not suited for beginners but it does an excellent job describing and explaining the algorithms in detail. The vivid images and well illustrated graphics make it easy to read and understand all the concepts. Although it uses Matlab to implement the models, it can be great read if you have some backround in mathematics.
Introduction to Machine Learning with Python - A Guide for Data Scientists
If you are a Developer or a Software Engineer with no experience in Machine Learning, then this is for you. Although it requires some familiarity with coding paradigms and Python, it is an excellent book to introduce you to Machine Learning and help you develop AI models in just a few weeks.
An Introduction to Statistical Learning - with Applications in R (Springer Texts in Statistics)
An amazing book about statistical learning and how it can be applied in science, industry and real-world applications. It presents the algorithms using the programming language R, so it can serve as a great manual for those with limited programming backround or those interested in exploring the R language.
Reinforcement Learning - An Introduction (Adaptive Computation and Machine Learning series)
It's very difficult to find a more comprehensive book around Reinforcement Learning. Although it was written in 1992, it tackles Reinforcement Learning in a very modern approach and leaves no question unanswered. Irreplaceable if you're looking something about Reinforcement Learning.
Computer Vision - Algorithms and Applications (Texts in Computer Science)
If you ask an expert in Computer Vision to suggest you a book, it will be probably be that one. Not very machine learning focused, but necessary to learn the basic principles and concepts behind Computer Vision.
Computer Vision - Models, Learning, and Inference
Another great Computer Vision read, which takes a more modern approach by exploring different machine learning techniques used in the field. It requires very little prerequisites and is suited for both practiotioners and researchers.
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