A comprehensive, self-contained guide to scikit-learn, the library that, more than any other, is how machine learning actually gets done in Python.

Welcome to Scikit-Learn: Practical Machine Learning in Python.

A comprehensive, self-contained guide to scikit-learn, the library that, more than any other, is how machine learning actually gets done in Python. Where the companion Machine Learning book teaches why the algorithms work — the mathematics of linear regression, SVMs, trees, and the rest — this book teaches how to wield them: the unified estimator API, the preprocessing and pipeline patterns that prevent subtle bugs, rigorous model selection and evaluation, and the end-to-end workflow that turns a dataset into a trustworthy, deployable model. It blends intuition, the concepts behind the API, and runnable code you can adapt immediately.

This title is part of the ShriIra library and is free to read in full, right here — our small contribution to making world-class knowledge easy to reach.

A note on reading it: open the Contents menu at the top of the reader to jump between chapters, use the Aa menu to set a comfortable text size, theme (light, sepia, or night), and single- or two-page layout. Your place is saved automatically, so you can always pick up where you left off.

We hope it serves you well.

— Shriira Press

Contents

1. Chapter 1 — What Is Scikit-Learn?
2. Chapter 2 — The Estimator API and Core Design
3. Chapter 3 — Data, Datasets, and the Train/Test Split
4. Chapter 4 — Regression
5. Chapter 5 — Classification
6. Chapter 6 — Trees, Forests, and Gradient Boosting
7. Chapter 7 — Support Vector Machines and Other Models
8. Chapter 8 — Preprocessing and Feature Engineering
9. Chapter 9 — Pipelines and Composite Estimators
10. Chapter 10 — Model Selection and Hyperparameter Tuning
11. Chapter 11 — Evaluation Metrics and Model Diagnosis
12. Chapter 12 — Unsupervised Learning: Clustering and Dimensionality Reduction
13. Chapter 13 — End-to-End, Production, and the Ecosystem
14. Appendix A — Glossary and API Quick Reference
15. Appendix B — Further Reading and Resources