Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.

  • Apple
  • Android
  • Windows Phone
  • Android

To get the free app, enter your email address or mobile phone number.

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 1st ed. 2013, Corr. 5th printing 2015 Edition

4.8 out of 5 stars 109 customer reviews
ISBN-13: 978-1461471370
ISBN-10: 1461471370
Why is ISBN important?
ISBN
This bar-code number lets you verify that you're getting exactly the right version or edition of a book. The 13-digit and 10-digit formats both work.
Scan an ISBN with your phone
Use the Amazon App to scan ISBNs and compare prices.
Trade in your item
Get a $32.05
Gift Card.
Have one to sell? Sell on Amazon
Rent On clicking this link, a new layer will be open
$18.54 - $20.22 On clicking this link, a new layer will be open
Buy used On clicking this link, a new layer will be open
$60.96 On clicking this link, a new layer will be open
Buy new On clicking this link, a new layer will be open
$69.71 On clicking this link, a new layer will be open
More Buying Choices
84 New from $45.07 58 Used from $54.07
Free Two-Day Shipping for College Students with Amazon Student Free%20Two-Day%20Shipping%20for%20College%20Students%20with%20Amazon%20Student


Amazon Book Review
The Amazon Book Review
Discover what to read next through the Amazon Book Review. Learn more.
$69.71 FREE Shipping. In Stock. Ships from and sold by Amazon.com. Gift-wrap available.
click to open popover

Frequently Bought Together

  • An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
  • +
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
  • +
  • Python Machine Learning
Total price: $189.80
Buy the selected items together

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)

“Written by four experts of the field, this book offers an excellent entry to statistical learning to a broad audience, including those without strong background in mathematics. … The end-of-chapter exercises make the book an ideal text for

both classroom learning and self-study. … The book is suitable for anyone interested in using statistical learning tools to analyze data. It can be used as a textbook for advanced undergraduate and master’s students in statistics or related quantitative fields.” (Jianhua Z. Huang, Journal of Agricultural, Biological, and Environmental Statistics, Vol. 19, 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)

NO_CONTENT_IN_FEATURE


Product Details

  • Series: Springer Texts in Statistics (Book 103)
  • Hardcover: 426 pages
  • Publisher: Springer; 1st ed. 2013, Corr. 5th printing 2015 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: 4.8 out of 5 stars  See all reviews (109 customer reviews)
  • Amazon Best Sellers Rank: #7,516 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Hardcover Verified Purchase
This is a wonderful book written by luminaries in the field. While it is not for casual consumption, it is a relatively approachable review of the state of the art for people who do not have the hardcore math needed for The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). This book is the text for the free Winter 2014 MOOC run out of Stanford called StatLearning (sorry Amazon will not allow me to include the website). Search for the class and you can watch Drs. Hastie and Tibshirani teach the material in this book.
12 Comments 139 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Hardcover Verified Purchase
The book provides the right amount of theory and practice, unlike the earlier (venerable and, by now, stable) text authored (partly) by the last two authors of this one (Elements of Statistical Learning), which was/is a little heavy on the theoretical side (at least for practitioners without a strong mathematical background). The authors make no pretense about this either. The Preface says "But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics."

ISL is neither as comprehensive nor as in-depth as ESL. It is, however, an excellent introduction to Learning due to the ability of the authors to strike a perfect balance between theory and practice. Theory is there to aim the reader as to understand the purpose and the "R Labs" at the end of each chapter are as valuable (or perhaps even more) than the end-of-chapter exercises.

ISL is an excellent choice for a two-semester advanced undergraduate (or early graduate) course, practitioners trained in classical statistics who want to enter the Learning space, and seasoned Machine Learners. It is especially helpful for getting the fundamentals down without being bogged down in heavy mathematical theory, a great way to kick-off corporate Learning units, or as an aid to help statisticians and learners communicate better.

A needed and welcome addition to the Learning literature, authored by some of the most well respected names in industry and academia. A classic in the making. Recommended unreservedly.
Read more ›
3 Comments 97 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Hardcover Verified Purchase
If you want to build a comprehensive machine learning library, this would be the first book to purchase. While it does cover all of the basics, it is not watered down by any means. (I had the same fear as BK Reader) I found the following to be especially helpful;

1. Straight talk - These experts come right and say which methods work best under which circumstances. While there are many fancy algorithms covered in the book, they highlight the advantages of the simpler ones.
2. Emphasis on subjects that are not heavily addressed in most ML books - They thoroughly cover the challenges of high-dimensionality, data cleaning, and standardization. They do not limit their attention to these subjects to one chapter. They bring them up continually throughout the book.
3. Expertise - Dr. Hastie and Dr. Tibshirani are two of the thought leaders in statistical learning. You can be assured that you are learning from the best.
4. Many levels of depth - While the book does cover the basics, it is not watered down by any means. (I had the same worry as BK Reader) There is a great deal for any student of statistics; beginner or advanced.
5. R code - You are given enough code and examples to gain confidence in your ability to independently perform excellent analysis and modeling.
6. The concepts are just plain exciting! - You will feel an excitement as you discover and re-discover the algorithms they present.

The book is a standard work along with Elements of Statistical Learning and Pattern Recognition and Machine Learning (the Bayesian approach). If you enjoy the book, you may also want to consider Applied Predictive Modeling. It has the same style and approach.
Comment 33 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Hardcover Verified Purchase
This book was used in my graduate level Machine Learning class (with certain readings/problems from the authors other more challenging book, The Elements of Statistical Learning).

I loved the class and loved the book. I thought the applications with R made it far more accessible and made it easier to learn. While I totally love the theoretical underpinnings, sometimes they aren't the best to learn right away and applying the ideas make it easier to grasp.

Rob Tibs & Trevor Hastie also had an online course offered through Stanford's EdX that ran the same time I was taking the course. It had videos of Trevor and Rob explaining the concepts in the order they were presented in the book. The course also included exercises and quizzes. The best part of the online course was that Rob & Trevor were absolutely hilarious. I loved their commentary and their personalities clashed in the most humorous way possible; it is very easy to see that they love what they do and love each other's company.

I'd totally recommend this book. Keep an eye out for the next offering on Stanford's online course web page; it makes it a lot more enjoyable.
Comment 20 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse

Most Recent Customer Reviews

Set up an Amazon Giveaway

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Amazon Giveaway allows you to run promotional giveaways in order to create buzz, reward your audience, and attract new followers and customers. Learn more
This item: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)