The second edition has been expanded to include the following topics of note: Sparse methods for classification and regression. Topics highlighted originally from the first edition include: This book is appropriate for anyone who wishes to use contemporary tools for data analysis. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. While the original has been around since 2013, the second edition was published very recently, and is now freely-available via PDF on the book’s website.Ī description, directly from the books’ website:Īs the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. The book, a staple of statistical learning texts, is accessible to readers of all levels, and can be read without much of an existing foundational knowledge in the area. An Introduction to Statistical Learning, with Applications in R, written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, is an absolute classic in the space.
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