An Introduction to Statistical Learning: with Applications in Python (ISLP) offers a clear and approachable overview of statistical learning, the core toolkit behind modern data analysis in fields such as biology, finance, marketing, and astrophysics. Building on the huge success of the original ISLR book (with R), this edition presents the same concepts and methods, but with hands‑on labs in Python.
The book introduces many of the most widely used modeling and prediction techniques, including:
- Linear and multiple regression
- Classification methods (logistic regression, LDA, QDA, etc.)
- Resampling methods such as cross‑validation and the bootstrap
- Shrinkage approaches including Ridge and Lasso
- Tree‑based methods: decision trees, random forests, boosting
- Support Vector Machines (SVMs)
- Unsupervised learning, including clustering
- An introduction to deep learning
- Survival analysis, multiple testing, and more
Throughout the book, color graphics and real‑world examples are used to illustrate ideas and to demonstrate how the methods work in practice. The text is written for both statisticians and non‑statisticians who want to apply cutting‑edge statistical learning techniques to their data.
Like the original ISLR (with R), a key strength of ISLP is that each chapter includes a practical lab section, now implemented in Python. These labs:
- Guide Python beginners step by step through data analysis workflows.
- Provide clean, reproducible examples for experienced users and data scientists.
- Use standard Python data‑science libraries to mirror real‑world practice.
Ideal for undergraduate and graduate courses, as well as for self‑study and professional reference, An Introduction to Statistical Learning with Applications in Python is a go‑to resource for anyone who wants to build a solid foundation in statistical learning while working directly in the Python ecosystem.
