Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow resource

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1060 stars · 420 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebooks containing notes and exercise solutions for Part I of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'. It covers ML fundamentals including classification, SVMs, decision trees, ensembles, and unsupervised learning using scikit-learn.

Use cases

• learn machine learning fundamentals from scratch
• study alongside the Hands-On ML book
• find solutions to Hands-On ML chapter exercises
• review scikit-learn concepts like SVMs and decision trees
• get runnable notebook examples for classic ML algorithms
• prepare for ML interviews with core concepts

When to choose

• you are reading the Hands-On ML book and want companion notes and solutions
• you prefer learning through hands-on Jupyter notebook examples
• you want a structured roadmap through ML fundamentals with scikit-learn

When to avoid

• you need production ML code or a maintained library
• you want coverage of the deep learning (Keras/TensorFlow) part of the book, which is not included
• you need a resource with an explicit open-source license

Facets

learning-resource · maturity maintenance

machine-learning deep-learning data-science machine-learning deep-learning artificial-intelligence data-science tutorials python cross-platform jupyter-notebooks scikit-learn tensorflow keras book-notes exercise-solutions education

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