The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) 2nd Edition
- Springer; 2nd edition
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour 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.
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Great to have if you want to learn the theories behind machine learning techniques
A great book to get you familiar with the theories behind modern machine learning techniques, but in reality, it has a little to do with implementation in practical models.
Full of Pleasant Surprises
I originally bought this book for coverage of some topics about which I know nothing. But having had it for just a week, I have been more impressed with what I have learned about stuff that I thought I knew. The material in Chapter 7 is one of several cases in point. The discussion of cross validation is great, and the way it is related to other aspects of the model selection problem is masterly. More generally, the book manages a nice balance between theory and application, and its scope is truly impressive. Thanks to authors and publisher.