An Introduction to Statistical Learning
with Applications in R
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Home 
Download the book PDF (corrected 7th printing)
Statistical Learning MOOC covering the entire ISL book offered by Trevor
Hastie and Rob Tibshirani. 

About this Book  
R Code for Labs  
Data Sets and Figures  
ISLR Package  
Get the Book  
Author Bios  
Errata 
This book
provides an introduction to statistical learning methods. It is aimed for upper
level undergraduate students, masters students and Ph.D. students in the
nonmathematical sciences. The book also contains a number of R labs with
detailed explanations on how to implement the various methods in real life
settings, and should be a valuable resource for a practicing data scientist.
For a more advanced treatment of these topics: The Elements of Statistical Learning.
Slides and videos for Statistical Learning MOOC by Hastie and Tibshirani available separately here. Slides and video tutorials related to this book by Abass Al Sharif can be downloaded here.
"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book. Larry Wasserman, Professor, Department of Statistics and Department of Machine Learning, CMU.
As a textbook for an introduction to data science through machine learning, there is much to like about ISLR. It’s thorough, lively, written at level appropriate for undergraduates and usable by nonexperts. It’s chock full of interesting examples of how modern predictive machine learning algorithms work (and don’t work) in a variety of settings." Matthew Richey, The American Mathematical Monthly, Vol. 123, No. 7 (AugustSeptember 2016).
Linear Regression? I covered that last year. Wake me up when we get to Support Vector Machines! Noah Mackey 