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 Title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
 Author(s) Trevor Hastie, Robert Tibshirani, Jerome Friedman
 Publisher: Springer; 2nd edition (2016); eBook (Online Corrected 12th printing  Jan 13, 2017)
 Hardcover: 745 pages
 eBook: PDF (764 pages)
 Language: English
 ISBN10: 0387848576
 ISBN13: 9780387848570
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Book Description
This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting  the first comprehensive treatment of this topic in any book.
About the Authors Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area.
 Machine Learning
 Statistics and SAS Programming
 Data Analysis and Data Mining
 Probability, Stochastic Process, Queueing Theory, etc.
 The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
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