Machine Learning Yearning by Andrew Ng. The goal of this book is to teach you how to make the numerous decisions needed with organizing a machine learning project.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Rob Tibshirani, and Jerome Friedman. This book helps you develop a deeper understanding of statistical learning and requires some mathematical and statistical sophistication. Also see the book’s website.
Machine Learning: A Probabilistic Perspective by Kevin P. Murphy. This book has wide coverage of many of the latest ML topics, with many examples and techniques. It is not quite as demanding as The Elements of Statistical Learning. Note there many minor errors and the mathematics is not always carefully done–get the 3rd printing or later, where many of the earlier errata have been corrected. Also see the book’s website.
Introduction to Machine Learning by Ethem Alpaydin. This book covers the basic ML algorithms by focusing on particular cases and examples, and is appropriate for beginners.
An Introduction to Statistical Learning: With Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani. This is a simplified version of The Elements of Statistical Learning: Data Mining, Inference, and Prediction, without the math but with many R examples. Also see the book’sweb site.
Pattern Recognition and Machine Learning by Chris Bishop. This book is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. You can download the book, but this download is for Microsoft-internal use only.
Predictive Analytics with Microsoft Azure Machine Learning by Roger Barga, Val Fontama, and Wee Hyong Tok. This book introduces Azure Machine Learning and describes practical applications such as customer churn analysis and predictive maintenance models.
Introduction to Data Mining. Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time.
The Art of R Programming by Norman Matloff. The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions.
Advanced R by Hadley Wickham. This book presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends.