An Introduction to Statistical Learning (ISLR) 2nd Ed by Gareth James et al.
An Introduction to Statistical Learning (ISLR) 2nd Ed. - Table of Contents
1. Introduction
2. Statistical Learning
3. Linear Regression
4. Classification
5. Resampling Methods
6. Linear Model Selection and Regularization
7. Moving Beyond Linearity
8. Tree-Based Methods
9. Support Vector Machines
10. Unsupervised Learning
What You Will Learn in An Introduction to Statistical Learning (ISLR) 2nd Ed.
"Introduction to Statistical Learning: with Applications in R, 2nd Edition" by "Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani" is a beginner-friendly guide to "statistical learning" and "machine learning". The book explains fundamental concepts for analyzing data, including "predictive modeling", regression, classification, and resampling methods. Its clear, approachable style makes complex topics accessible, helping readers understand both the intuition and underlying theory.
The book emphasizes practical application using the "R programming" language. Readers learn how to implement algorithms and analyze real-world datasets through hands-on examples and exercises. Key topics include linear and logistic regression, tree-based methods, cross-validation, bootstrapping, and dimensionality reduction. By combining theory with practice, the book equips learners with the skills to explore data, build models, and interpret results effectively.
Ideal for students, researchers, and data science practitioners, ISLR 2nd Edition provides a solid foundation for understanding modern "data analysis" and prepares readers to advance to more complex methods, such as those in "The Elements of Statistical Learning". With its mix of intuition, theory, and practical examples, the book is an essential resource for anyone looking to master the basics of predictive modeling and machine learning.
Book Details & Specifications
Title:
An Introduction to Statistical Learning (ISLR) 2nd Ed by Gareth James et al.
Publisher:
Springer
Year:
2021
Pages:
441
Type:
PDF
Language:
English
ISBN-10 #:
1461471370
ISBN-13 #:
978-1461471370
License:
External Educational Resource
Amazon:
Amazon
About the Author: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
The author
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
are leading experts in "statistical learning" and "data science". James is a professor and dean focusing on high-dimensional data and predictive modeling. Witten, a biostatistician, specializes in machine learning for complex biological and genomic datasets. Hastie, born in South Africa, and Tibshirani, born in Canada, are Stanford professors known for their work in "regression", non-parametric methods, and the "lasso" technique. Together, they authored "An Introduction to Statistical Learning (ISLR) 2nd Ed", a foundational "machine learning" textbook. It guides researchers and practitioners in building interpretable "models" for real-world data analysis, combining theory, applications, and practical insights.
Read or Downloadable An Introduction to Statistical Learning (ISLR) 2nd Ed.
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