- Series: Springer Texts in Statistics (Book 103)
- Hardcover: 426 pages
- Publisher: Springer; 1st ed. 2013, Corr. 6th printing 2016 edition (August 12, 2013)
- Language: English
- ISBN-10: 1461471370
- ISBN-13: 978-1461471370
- Product Dimensions: 6.3 x 1 x 9.3 inches
- Shipping Weight: 2 pounds (View shipping rates and policies)
- Average Customer Review: 4.7 out of 5 stars See all reviews (140 customer reviews)
- Amazon Best Sellers Rank: #5,285 in Books (See Top 100 in Books)
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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 1st ed. 2013, Corr. 6th printing 2016 Edition
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“This book by James, Witten, Hastie, and Tibshirani was a great pleasure to read, and I was extremely surprised by it and the available material. In my opinion, it is the best book for teaching statistical learning approaches to undergraduate and master students in statistics. … All in all, this is a great textbook for teaching an introductory course in statistical learning. … In my opinion, there is no better book for teaching modern statistical learning at the introductory level.” (Andreas Ziegler, Biometrical Journal, Vol. 58 (3), May, 2016)
“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)
"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)
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Top Customer Reviews
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.
UPDATE (12/17/2013): Two of the authors (Hastie & Tibshirani) are offering a 10-week free online course (StatLearning: Statistical Learning) based on this book found at Stanford University's Web site (Starting Jan. 21, 2014). They also say that "As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website." Amazing opportunity! Enjoy!
UPDATE (04/03/2014): I took the course above and found it very helpful and insightful. You don't need the course to understand the book. If anything, the course videos are less detailed than the book. It is certainly nice, though, to see the actual authors explain the material. Also, the interviews by Efron and Friedman were a nice touch. The course will be offered again in the future.
The authors have done an outstanding job of taking complex topics and making them very understandable and quite frankly enjoyable. It also gives end of chapter exercises to practice all concepts that are covered. As others have stated, you can take the course for the book online. If you don't want to wait until the next course on the Stanford website opens up you can also use the link below for access to the youtube videos of the course lectures/powerpoints.
I don't think I could have accomplished as much without the help of this book. I was working with miRNA data, and I read through the chapters I needed to understand the classification models I wanted to work with.
The R code examples are very accessible and very useful. It probably saved me a lot of Google searches and headaches.
The authors of this book are extremely intelligent and pragmatic, and the writing is accessible to anyone. I never took any statistics classes. While I found it helpful (and I would say necessary) to learn at least some Stat 101 material to give intuition to the concepts in this book, the material is otherwise self contained.
The PDF is free online, and I prefer PDF format, but I loved the book so much that I bought the Kindle version off of Amazon to support the authors.
I would like to read the advanced, supplementary version when I have more advanced probability, statistics, and linear algebra knowledge.
1. This book is focused on the statistical learning part, with concepts, applications and R code
2. Chapters are very well organized with increasing order of difficulty
3. Its almost like reading a story. Each figure and equation are weaved into the story
4. R examples were a breeze. I had no knowledge of R language but I did not have any problems
I also enjoy rereading this book every time. :-)