shunliz/Machine-Learning resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1428 stars · 304 forks observed · 2026-08-28

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

A curated collection of study notes on machine learning principles, published as a Gitbook, covering mathematical foundations (calculus, probability, linear algebra) and machine learning/deep learning theory with detailed formula derivations. The latter half focuses on engineering practice, including Python ML libraries, feature engineering, and model evaluation.

Use cases

• learn the math behind machine learning algorithms
• study gradient descent and convex optimization derivations
• understand MCMC and Gibbs sampling
• review feature engineering and dimensionality reduction techniques like PCA and SVD
• find notes on scikit-learn, TensorFlow, and Spark usage
• prepare for machine learning interviews

When to choose

• you want free, comprehensive notes with detailed mathematical derivations
• you prefer reading structured theory alongside practical Python examples
• you can read Chinese-language technical material

When to avoid

• you need a maintained software library or runnable codebase
• you want original, licensed content with clear attribution
• you need up-to-date coverage of modern LLM-era techniques

Facets

learning-resource · maturity maintenance

machine-learning deep-learning data-science math machine-learning deep-learning data-science tutorials mathematics python cross-platform notes gitbook chinese-language theory math-foundations study-guide

Member repositories

For agents

markdown · JSON · MCP: product_card(name="shunliz/Machine-Learning")