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Scikit-learn Tutorial contains the materials for Jake VanderPlas’s introductory scikit-learn tutorial, originally used at major Python conferences. It provides a collection of notebooks that walk attendees from basic machine-learning concepts into practical modeling using the scikit-learn library. The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. The repository specifies a clear list of dependencies so that participants can reproduce the environment used in the tutorial, and many downstream forks keep the content updated for newer versions of scikit-learn. Although the GitHub repository has been archived and is read-only, it is still a valuable snapshot of early, hands-on teaching material for scikit-learn and machine learning in Python.
Features
- Hands-on Jupyter notebooks introducing scikit-learn in a workshop format
- Coverage of core ML tasks such as classification, regression, clustering, and model evaluation
- Explicit dependency list for Python, NumPy, SciPy, Matplotlib, scikit-learn, IPython, and Seaborn
- Designed to pair with recorded conference tutorial videos for self-paced learning
- Serves as a reference template for other organizations creating ML workshops
- Archived for stability, preserving a consistent snapshot of the original tutorial content
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