luwill/Machine_Learning_Code_Implementation resource

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1551 stars · 586 forks observed · 2026-08-28

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

A companion code repository for a Chinese machine learning book that provides mathematical derivations and pure Python implementations of 26 classic algorithms across supervised, ensemble, unsupervised, and probabilistic models. It supplements textbooks like 'Statistical Learning Methods' and Zhou Zhihua's 'Machine Learning' (watermelon book) with runnable Jupyter Notebook code organized by chapter.

Use cases

• learn machine learning algorithms from scratch with math derivations
• find pure Python implementations of classic ML algorithms
• supplement study of the watermelon book or Statistical Learning Methods
• study perceptron, logistic regression, LDA, HMM, MCMC implementations
• get example code for supervised and unsupervised learning models
• prepare for ML interviews by understanding algorithm internals

When to choose

• you want to understand the math behind ML algorithms alongside working code
• you are studying Chinese ML textbooks and need a code companion
• you prefer minimal pure Python implementations over heavy framework code

When to avoid

• you need production-ready, well-tested ML libraries
• you want GPU-accelerated or deep learning frameworks
• you need English-language documentation

Facets

learning-resource · maturity active

machine-learning developer-tools machine-learning education tutorials python jupyter-notebook algorithm-derivations companion-code textbook educational

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