An Introduction to Statistical Learning with Applications in R, written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.

**Description**

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

**Table of Contents**

- Introduction
- Statistical Learning
- Linear Regression
- Classification
- Resampling Methods
- Linear Model Selection and Regularization
- Moving Beyond Linearity
- Tree-Based Methods
- Support Vector Machines
- Unsupervised Learning

**Book Details**

Publisher: Springer

Format(s): PDF

File size: 9.00 MB

Number of pages: 440

Link: Download.