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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) by [Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani]

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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) [Print Replica] Kindle Edition

4.6 out of 5 stars 169 ratings

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"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)

--This text refers to the hardcover edition.

About the Author

Gareth James  is a professor of data sciences and operations, and the E. Morgan Stanley Chair in Business Administration, at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.

Daniela Witten  is a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at the University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning.

Trevor Hastie andRobert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.      

--This text refers to the hardcover edition.

Product details

  • ASIN ‏ : ‎ B09BHG37HZ
  • Publisher ‏ : ‎ Springer; 2 edition (July 29 2021)
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 23192 KB
  • Text-to-Speech ‏ : ‎ Not enabled
  • Enhanced typesetting ‏ : ‎ Not Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Sticky notes ‏ : ‎ Not Enabled
  • Print length ‏ : ‎ 607 pages
  • Customer Reviews:
    4.6 out of 5 stars 169 ratings

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4.6 out of 5 stars
4.6 out of 5
169 global ratings

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5.0 out of 5 stars If you know a little this will add a lot
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5.0 out of 5 stars Best book on this subject
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5.0 out of 5 stars very nice book
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Arturo Sbr
5.0 out of 5 stars Greatest Data Science book ever (coming from someone who hates R)
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5.0 out of 5 stars Great book
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