# An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) by Gareth James

## Book Description – An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Product Details Series: Springer Texts in Statistics (Book 103) Hardcover: 426 pages

Publisher: Springer; 1st ed. 2013. Corr. 4th printing 2014 edition (August 12, 2013) Language: English

ISBN-10: 1461471370

ISBN-13: 978-1461471370

Product Dimensions: 9.4 x 6.4 x 1 inches Shipping Weight: 2 pounds (View shipping rates and policies) Average Customer Review: 4.9 out of 5 stars See all reviews (55 customer reviews) Amazon Best Sellers Rank: #3,213 in Books (See Top 100 in Books) #1 in Books > Computers & Technology > Software > Mathematical & Statistical #1 in Books > Textbooks > Computer Science > Artificial Intelligence #2 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Intelligence & Semantics Would you like to update product info or give feedback on images?

## Editorial Reviews

### Review

From the book reviews:

“This book has a very strong advantage that sets it well ahead of the competition when it comes to learning about machine learning: it covers all of the necessary details that one has to know in order to apply or implement a machine learning algorithm in a real-world problem. Hence, this book will definitely be of interest to readers from many fields, ranging from computer science to business administration and marketing.” (Charalambos Poullis, Computing Reviews, September, 2014)

“The book provides a good introduction to R. The code for all the statistical methods introduced in the book is carefully explained. … the book will certainly be useful to many people (including me). I will surely use many examples, labs and datasets from this book in my own lectures.” (Pierre Alquier, Mathematical Reviews, July, 2014)

“The stated purpose of this book is to facilitate the transition of statistical learning to mainstream. … it adds information by including more detail and R code to some of the topics in Elements of Statistical Learning. … I am having a lot of fun playing with the code that goes with book. I am glad that this was written.” (Mary Anne, Cats and Dogs with Data, maryannedata.com, June, 2014)

“This book (ISL) is a great Master’s level introduction to statistical learning: statistics for complex datasets. … the homework problems in ISL are at a Master’s level for students who want to learn how to use statistical learning methods to analyze data. … ISL contains 12 very valuable R labs that show how to use many of the statistical learning methods with the R package ISLR … .” (David Olive, Technometrics, Vol. 56 (2), May, 2014)

“It aims to introduce modern statistical learning methods to students, researchers and practitioners who are primarily interested in analysing data and want to be confined only with the implementation of the statistical methodology and subsequent interpretation of the results. … the book also demonstrates how to apply these methods using various R packages by providing detailed worked examples using interesting real data applications.” (Klaus Nordhausen, International Statistical Review, Vol. 82 (1), 2014)

“The book is structured in ten chapters covering tools for modeling and mining of complex real life data sets. … The style is suitable for undergraduates and researchers … and the understanding of concepts is facilitated by the exercises, both practical and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. 1281, 2014)

“The book excels in providing the theoretical and mathematical basis for machine learning, and now at long last, a practical view with the inclusion of R programming examples. It is the latter portion of the update that I’ve been waiting for as it directly applies to my work in data science. Give the new state of this book, I’d classify it as the authoritative text for any machine learning practitioner…This is one book you need to get if you’re serious about this growing field.” (Daniel Gutierrez, Inside Big Data, inside-bigdata.com, October 2013)

### Review

“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 Machine Learning Department, Carnegie Mellon University)