# MLEveryday/100-Days-Of-ML-Code

100-Days-Of-ML-Code中文版

Repository: https://github.com/MLEveryday/100-Days-Of-ML-Code
Canonical: https://ross.abutalabs.com/products/mleveryday-100-days-of-ml-code
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, python, 100-days-of-ml-code, chinese-simplified, infographics, tutorial, jupyter-notebook, tensorflow, keras, supervised-learning, unsupervised-learning, deep-learning
Last push: 2022-04-06T12:01:37+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2950, "days_push": 1610, "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 22232, forks 5480 (observed 2026-08-28T04:11:32.488906+00:00)

## What it is
A Chinese translation of the popular 100-Days-Of-ML-Code learning challenge, providing day-by-day machine learning tutorials with infographics and Jupyter notebook implementations. It covers supervised and unsupervised learning topics such as linear regression, logistic regression, k-NN, SVM, decision trees, random forests, and clustering.

## Use cases
- learn machine learning in 100 days
- find machine learning tutorials in Chinese
- study supervised learning algorithms with code examples
- get infographics explaining ML concepts
- practice ML algorithms in Jupyter notebooks
- learn regression, SVM, k-NN, and clustering step by step

## When to choose
- you are a beginner wanting a structured day-by-day ML curriculum
- you prefer Chinese-language explanations of ML concepts
- you learn well from infographics paired with runnable notebook code

## When to avoid
- you need up-to-date coverage of modern deep learning frameworks
- you want production-grade ML code rather than educational examples
- you need the original English version of the challenge

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, tutorials, education
- platform: python, cross-platform
- tags: 100-days-of-ml-code, chinese-translation, infographics, jupyter-notebook, supervised-learning, unsupervised-learning, tutorial

## Member repositories
- MLEveryday/100-Days-Of-ML-Code (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:32.488906+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 2026-08-29T16:57:41.373718+00:00, confidence not recorded.
  - readme: https://github.com/MLEveryday/100-Days-Of-ML-Code (fetched 2026-08-28T04:11:32.488906+00:00, sha aa29e3f7c584)
- Data as of 2026-08-30T08:39:29.467469+00:00.