MLEveryday/100-Days-Of-ML-Code

100-Days-Of-ML-Code中文版

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This resource helps individuals understand and implement various machine learning algorithms. It provides a structured learning path through different techniques like linear regression, decision trees, and K-means clustering. Each 'day' offers explanations and practical code examples, enabling aspiring data scientists and machine learning enthusiasts to build foundational skills from raw data to actionable models.

22,212 stars. No commits in the last 6 months.

Use this if you are a student, hobbyist, or professional looking for a structured, hands-on approach to learning machine learning concepts and their practical application.

Not ideal if you are an experienced machine learning practitioner seeking advanced topics or a deep dive into theoretical research without practical examples.

machine-learning-education data-science-fundamentals supervised-learning unsupervised-learning algorithm-implementation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 25 / 25

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Stars

22,212

Forks

5,543

Language

Jupyter Notebook

License

MIT

Last pushed

Apr 06, 2022

Commits (30d)

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