# datawhalechina/machine-learning-toy-code

《机器学习》（西瓜书）代码实战

Repository: https://github.com/datawhalechina/machine-learning-toy-code
Canonical: https://ross.abutalabs.com/products/machine-learning-toy-code
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, ml
Last push: 2025-05-07T07:11:32+00:00

## Health v2 (maintenance only)
Score: 41/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 20, release rhythm 35, longevity 100
- inputs: {"age_days": 1874, "days_push": 483, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1004, forks 201 (observed 2026-09-01T02:14:00.755744+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, data-science, data-visualization
- domain: machine-learning, education, tutorials, data-science
- platform: python, cross-platform
- tags: jupyter-notebooks, watermelon-book, sklearn, chinese, datawhale, hands-on-exercises

## Member repositories
- datawhalechina/machine-learning-toy-code (main) score 41

## Provenance
- Observed fields: from GitHub, fetched 2026-09-01T02:14:00.755744+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:13:50.422710+00:00, confidence not recorded.
  - readme: https://github.com/datawhalechina/machine-learning-toy-code (fetched 2026-09-01T02:14:00.755744+00:00, sha 1d25f651cb73)
- Data as of 2026-08-30T08:39:29.467469+00:00.