datawhalechina/machine-learning-toy-code resource

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1004 stars · 201 forks observed · 2026-09-01

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

A collection of Jupyter Notebook tutorials implementing classic machine learning algorithms from the 'Watermelon Book' (Zhou Zhihua's Machine Learning textbook), pairing math formulas with sklearn-based code. It covers 13 algorithm chapters plus links to data competition practice projects.

Use cases

• learn machine learning algorithms with code alongside the watermelon book
• understand how math formulas map to sklearn implementations
• practice linear regression, SVM, decision trees and other classic ML algorithms
• find hands-on notebooks for studying machine learning fundamentals
• prepare for data mining competitions after learning ML theory
• study HMM, K-means, PCA with worked examples

When to choose

• you are studying the Watermelon Book or Pumpkin Book and want matching code
• you learn best by reading formulas next to runnable sklearn examples
• you want a structured, chapter-by-chapter ML practice course

When to avoid

• you need production-ready ML code or a reusable library
• you want deep learning or modern transformer-based tutorials
• you cannot read Chinese, as the material is written in Chinese

Facets

learning-resource · maturity stable

machine-learning data-science data-visualization machine-learning education tutorials data-science python cross-platform jupyter-notebooks watermelon-book sklearn chinese datawhale hands-on-exercises

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