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

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22232 stars · 5480 forks observed · 2026-08-28

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

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

learning-resource · maturity maintenance

machine-learning deep-learning data-science machine-learning deep-learning tutorials education python cross-platform 100-days-of-ml-code chinese-translation infographics jupyter-notebook supervised-learning unsupervised-learning tutorial

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