Scikit-Learn Examples
Gallery of scikit-learn examples covering classification, regression, clustering, decomposition, and more.
Use this folder as a model-building workshop, not as a checklist to clear. It is large by design. The goal is to learn the core workflow well enough to train, validate, compare, and explain models on realistic problems.
What This Folder Is For
- Supervised learning workflows for classification and regression
- Preprocessing, feature engineering, and model selection
- Clustering, decomposition, and unsupervised exploration
- Evaluation habits that carry into later AI and ML phases
How To Use This Folder Well
- Start with
linear_model,model_selection,preprocessing, andensemblebefore branching out. - Build one full baseline-first workflow instead of sampling many isolated notebooks.
- Pay close attention to train/test splits, leakage, metrics, and error analysis.
- Treat this folder as practice for thinking clearly about experiments, not just for importing estimators.
What Comes Next
- Continue to ../../03-maths/README.md if you want stronger theoretical grounding behind the models you use here.
- Continue to ../../09-mlops/README.md if you want to package and deploy classical ML systems.
- Continue to ../../28-practical-data-science/README.md if you want end-to-end applied project work.
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