zhulei227/ML_Notes resource

Health v2 · maintenance only

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2128 stars · 481 forks observed · 2026-08-28

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

A collection of Jupyter Notebook tutorials covering mainstream machine learning algorithms with mathematical formula derivations, problem analysis, and from-scratch implementations in numpy. Topics include linear models, SVM, decision trees, ensemble learning, and optimization methods like quasi-Newton algorithms.

Use cases

• learn machine learning algorithm derivations from scratch
• understand how SVM and SMO work mathematically
• implement decision trees like ID3, C4.5, and CART in numpy
• study boosting and ensemble learning internals
• see numpy implementations of linear and logistic regression
• learn quasi-Newton optimization methods like DFP and BFGS

When to choose

• you want to deeply understand the math behind ML algorithms rather than just call sklearn
• you prefer learning through runnable notebooks with derivations and code side by side
• you are comfortable reading Chinese-language technical material

When to avoid

• you need production-ready, well-tested ML libraries
• you require English documentation
• you need deep learning or modern transformer-based content

Facets

learning-resource · maturity maintenance

machine-learning math data-science machine-learning tutorials data-science python jupyter-notebooks numpy-implementations formula-derivations educational chinese-language svm decision-trees ensemble-learning algorithms

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