Read An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Online
Read [Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Book] # An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) Online ^ PDF eBook or Kindle ePUB free. An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) wonderful but watch the movie 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 201wonderful but watch the movie I Teach Typing This is a wonderful book written b
| Title | : | An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) |
| Author | : | |
| Rating | : | 4.46 (825 Votes) |
| Asin | : | 1461471370 |
| Format Type | : | paperback |
| Number of Pages | : | 426 Pages |
| Publish Date | : | 2014-04-11 |
| Language | : | English |
Color graphics and real-world examples are used to illustrate the methods presented. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. 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 presents some of the most important modeling and prediction techniques, along with relevant applications. 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 f
wonderful but watch the movie 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 201wonderful but watch the movie I Teach Typing 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 w. MOOC run out of Stanford called StatLearning (sorry Amazon w. Excellent Practical Introduction to Learning 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 . Joseph Johnson said cover all of your bases. 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 advantage
… The book is suitable for anyone interested in using statistical learning tools to analyze data. The code for all the statistical methods introduced in the book is carefully explained. I am glad that this was written.” (Mary Anne, Cats and Dogs with Data, maryannedata, June, 2014)“This book (ISL) is a great Master’s level introduction to statistical learning: statistics for complex datasets. 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 inclusio
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