An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

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) This book presents some of the most important modeling and prediction techniques, along with relevant applications. 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 pres

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

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Rating : 4.50 (831 Votes)
Asin : 1461471370
Format Type : paperback
Number of Pages : 426 Pages
Publish Date : 2013-06-28
Language : English

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This book presents some of the most important modeling and prediction techniques, along with relevant applications. 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 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. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.. Color graphics and real-world examples are used to illustrate the methods presented. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. Topics include linear regres

wonderful 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 will not allow me to include the website). Search for the class and you can watch Drs. Hastie . Excellent Practical Introduction to Learning Michael Tsiappoutas 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 in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the pe. 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 advantages of the simpler ones.2. Emphasis on subjects that are not heavily addressed in most ML books

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. … In my opinion, there is no better book for teaching modern statistical learning at the introductory level.” (Andreas Ziegler, Biometrical Journal, Vol. … 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. 1281, 2014) "The book excels

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