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Statistics · Statistical Learning
An Introduction to Statistical Learning (ISLR)
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani · 2nd edition · 2021
🆓 FreeIntermediate★★★★★607 pagesISBN 978-1071614174
The most popular statistics-for-ML textbook. Free PDF. Covers regression, classification, resampling, model selection, and unsupervised learning with R and Python.
Why you should read this
The perfect balance between math rigor and practical application. Free, accessible, and universally respected.
The perfect balance between math rigor and practical application. Free, accessible, and universally respected.
Key Topics
Chapters (8)
1Introduction6 key concepts
Statistical learning overview
2Statistical Learning12 key concepts
Prediction vs inference, bias-variance
3Linear Regression16 key concepts
Simple and multiple regression, diagnostics
4Classification14 key concepts
Logistic regression, LDA, QDA, KNN
5Resampling Methods8 key concepts
Cross-validation, bootstrap
6Model Selection14 key concepts
Subset selection, ridge, lasso, elastic net
7Beyond Linearity10 key concepts
Polynomial, splines, GAMs, local regression
8Tree-Based Methods14 key concepts
Decision trees, bagging, random forests, boosting
Real-World Applications
- Credit scoring
- Medical diagnosis
- Marketing analytics
- Sports analytics
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