• An Introduction to Statistical Learning: with Applications in R (2nd Edition)

• Authors: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
• Edition: 2nd
• ISBN-13: 9781071614174
• Language: English
• Focus: Statistical learning methods with applications in R
• Level: Suitable for students and practitioners with limited prior experience in statistics or machine learning.
• Content Highlights: Covers linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and deep learning.
• Software: Emphasizes practical application using the R programming language.
• New in 2nd Edition: Includes new chapters on deep learning, survival analysis, causal inference, and ethical issues in statistical learning, with revisions and updates throughout.
• Applications: Features numerous examples and case studies illustrating real-world applications.
• Target Audience: Ideal for advanced undergraduate and master's students in statistics, data science, and related fields, as well as practitioners.

An Introduction to Statistical Learning (ISL), Second Edition, offers an accessible and broad overview of key statistical learning topics. Suitable for anyone using contemporary data analysis tools, ISL balances statistical theory with practical examples using R. The Second Edition includes new chapters on deep learning, survival analysis, and causal inference, alongside significant updates. Emphasizing application, the book enables readers to effectively implement statistical learning techniques, even with limited prior knowledge. It equips students and professionals to tackle complex data challenges through clear explanations and real-world case studies.