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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) Hardcover – 12 Aug 2013


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Product details

  • Hardcover: 426 pages
  • Publisher: Springer; 1st ed. 2013. Corr. 4th printing 2014 edition (12 Aug. 2013)
  • Language: English
  • ISBN-10: 1461471370
  • ISBN-13: 978-1461471370
  • Product Dimensions: 23.9 x 16.3 x 2.5 cm
  • Average Customer Review: 4.7 out of 5 stars  See all reviews (6 customer reviews)
  • Amazon Bestsellers Rank: 106,425 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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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)


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By J. Rennie on 20 Oct. 2014
Format: Hardcover
I would give this 6 stars if I could. Really excellent.

I read this from start to end. Clear descriptions in plain English with some maths where necessary. I've been interested in machine learning and related topics for some months now but approaching it from the perspective of a programmer and not especially keen on wading through pages of formulas and proofs. I coul never quite work out how the different methods were related and I found the consistency of this book to be a revelation. Too many books get bogged down in dense mathematical notation and I find this extremely difficult to follow after a while. And in other books quite often the level of detail is not necessary for someone like myself who just needs to understand how these methods work enough that I can use them effectively. This book told me what I needed to know without getting submerged in proofs and mathematical notation. The authors are clearly excellent teachers because I frequently found myself puzzled by something only to realise that the next paragraph explained exactly what I was wondering.

Another good thing is that the authors often say "the mathematics of this are beyond the scope of this book" and then leave it to the lab in R to give clear examples showing what you need to know. The rest of the text has explained enough. And for practitioners like myself the implementation details are unimportant when the library functions give me all I need.

I'd read about many of the topics covered by this book individually before and found it hard to see how they progressed and how they were connected. This book laid it out in exactly the level of detail that I was looking for. It joined the dots and I properly understood how this stuff works. The Labs sections in R were at a good level of detail and shown me how to get started with my implementation.

I'm now scouring Amazon for other books from Springer :)
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Format: Hardcover
This book (and the accompanying free course on Stanford University's website) is an excellent introduction to the most common statistical learning techniques for people who want to use them, but don't need to be able to derive all the formulas or use the proofs. There is enough detail that the reader will learn the limits and drawbacks of each technique described, and will know how to choose which to use. Those with an advanced mathematical background and involved in developing new techniques will find this text limited. They would be better reading the 'Elements of Statistical Learning by the same authors. The R tutorials are effective in putting the theory into practice.
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Format: Hardcover
I've just read 2-3 chapters of this great book and am hugely impressed with the clarity, simplicity of the writing explaining what is often a technically challenging area. Fully explained via examples, I'll post a full review when I've finished the book but it is simply so good I felt I had to comment now.
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