Machine Learning


An Introduction to Statistical Learning: with Applications in R

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.


The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

 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.


Building Machine Learning Systems with Python

With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge - by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can 'learn' from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data.