An Introduction to Statistical Learning: with Applications in R Gareth James, Daniela Witten, and Trevor Hastie is an English-language textbook that provides a clear overview of statistical learning and analysis techniques. This book focuses on applying statistical methods across a variety of fields and is suitable for students and professionals who want to deepen their knowledge of statistical learning.
The work covers important topics such as linear regression, classification, resampling, shrinkage, tree-based methods, support vector machines, clustering, and deep learning. Practical applications and colorful graphics support understanding of the techniques. In addition, each chapter includes a tutorial for using R, a popular open-source software for statistical analysis.
An Introduction to Statistical Learning offers an accessible introduction to these advanced methods and is written by leading authors in the field. The book is valuable for anyone who wants to delve into statistical learning techniques and data analysis.
It connects with topics such as Probability and statistics, Mathematical and statistical software, MATHEMATICS / Probability & Statistics / General.
Series: Springer Texts in Statistics