Introduction to Statistical Learning

Books
Statistical learning
Classical ML
Regression
Classification
Deep learning
Decision trees
Unsupervised Learning
Code-along
Author

Chris Endemann

Published

January 6, 2025

About this resource

An Introduction to Statistical Learning (ISL) is a foundational book for anyone interested in statistical learning and its applications in data analysis. Authored by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani, ISL provides an approachable yet comprehensive introduction to the field, covering critical topics like regression, classification, tree-based methods, and deep learning. It is designed to cater to readers with minimal mathematical background, making it suitable for a broad audience. Each chapter includes practical labs that use R or Python to solidify concepts through hands-on coding.

Statistical learning, a term predating the widespread use of machine learning, emphasizes rigorous statistical frameworks and interpretability. While the term is less common today, it remains central in academic and statistical contexts. ISL bridges statistical learning with modern ML approaches, offering readers a practical foundation in both.

The first edition of ISL (with R) was published in 2013, and a second edition followed in 2021. In 2023, a Python edition was released, broadening the book’s accessibility to users of different programming languages. This resource has been translated into multiple languages and remains a cornerstone text for learning statistical learning methods.

From the authors

As 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. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

Prerequisites

  • Basic understanding of statistics and linear algebra
  • Familiarity with R or Python is helpful for the labs

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Questions?

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See also