An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics Book 103) 1st ed. 2013, Corr. 7th printing 2017 Edition, Kindle Edition

4.8 out of 5 stars 1,353 ratings
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ISBN-13: 978-1461471370
ISBN-10: 1461471370
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Editorial Reviews

From the Back Cover

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

--This text refers to an alternate kindle_edition edition.

Review

“Data and statistics are an increasingly important part of modern life, and nearly everyone would be better off with a deeper understanding of the tools that help explain our world. Even if you don’t want to become a data analyst―which happens to be one of the fastest-growing jobs out there, just so you know―these books are invaluable guides to help explain what’s going on.” (Pocket, February 23, 2018)

--This text refers to an alternate kindle_edition edition.

Product details

  • ASIN ‏ : ‎ B00DM0VX60
  • Publisher ‏ : ‎ Springer; 1st ed. 2013, Corr. 7th printing 2017 edition (June 24, 2013)
  • Publication date ‏ : ‎ June 24, 2013
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 4169 KB
  • Text-to-Speech ‏ : ‎ Enabled
  • Enhanced typesetting ‏ : ‎ Not Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Print length ‏ : ‎ 440 pages
  • Lending ‏ : ‎ Not Enabled
  • Customer Reviews:
    4.8 out of 5 stars 1,353 ratings

Customer reviews

4.8 out of 5 stars
4.8 out of 5
1,353 global ratings
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Reviewed in the United States on December 16, 2017
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Reviewed in the United States on June 4, 2017
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Reviewed in the United States on February 13, 2014
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Reviewed in the United States on December 2, 2018
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Rahul Madhavan
5.0 out of 5 stars Love hate relationship with this book. Yet, a 5 rating with a recommended buy
Reviewed in India on June 4, 2019
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118 people found this helpful
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wooMonkey
2.0 out of 5 stars You need a bit of maths/stats knowledge beforehand
Reviewed in the United Kingdom on March 10, 2020
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ZigZag
5.0 out of 5 stars This is one of the best books on the cutting edge between statistics and machine ...
Reviewed in the United Kingdom on March 6, 2018
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Docpappy
5.0 out of 5 stars Best statistics book I've read
Reviewed in the United Kingdom on December 12, 2018
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W. M. A.
1.0 out of 5 stars Great book, poor delivery.
Reviewed in the United Kingdom on October 24, 2019
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2 people found this helpful
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