Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow is a collection of Jupyter notebooks that teaches machine learning and deep learning fundamentals in Python.
The project addresses the need for practical, hands-on learning material that bridges theory and implementation. It provides executable notebook-based tutorials covering machine learning concepts using Scikit-Learn for traditional algorithms and Keras with TensorFlow 2 for deep learning. The notebooks include example code and exercise solutions aligned with the third edition of the accompanying O'Reilly book, allowing learners to follow along with working implementations rather than reading abstract explanations.
This resource suits developers and students seeking to build foundational machine learning skills through direct code interaction. The project offers multiple ways to engage with the material: running notebooks online through Google Colab without local installation, viewing them statically through nbviewer for quick reference, executing them locally via Anaconda, or using Docker for containerized environments. The approach works well for self-paced learning where readers can experiment with code examples immediately while studying concepts.
The project maintains active engagement with its material through regular updates to keep notebooks functional with current library versions. The codebase receives ongoing refinement to ensure examples remain accurate and executable across different Python environments and dependency versions. Documentation is comprehensive, covering installation paths for multiple platforms and providing troubleshooting guidance for common setup issues.
