fengdu78/lihang-code resource

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19590 stars · 6238 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of Jupyter Notebook code implementations accompanying Li Hang's textbook 'Statistical Learning Methods' (2nd edition). It covers classic supervised and unsupervised algorithms such as perceptron, kNN, naive Bayes, decision trees, logistic regression, SVM, boosting, EM, HMM, CRF, clustering, SVD, PCA, LDA, MCMC, and PageRank.

Use cases

• learn machine learning algorithms by reading runnable code
• study the algorithms from the Statistical Learning Methods textbook
• implement classic ML algorithms from scratch in Python
• prepare for machine learning interviews
• find reference implementations of SVM, EM, HMM, or LDA

When to choose

• you are studying Li Hang's Statistical Learning Methods book and want code for each chapter
• you want simple, educational from-scratch implementations of classic ML algorithms
• you prefer learning through Jupyter notebooks

When to avoid

• you need production-ready, optimized, or well-tested ML code
• you want a maintained library with an API rather than educational notebooks
• you need deep learning or modern large-scale methods

Facets

learning-resource · maturity maintenance

machine-learning data-science machine-learning education tutorials python jupyter-notebooks statistical-learning textbook-companion chinese supervised-learning unsupervised-learning

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