rushter/MLAlgorithms
Minimal and clean examples of machine learning algorithms implementations
This project helps software developers and machine learning engineers understand how core machine learning algorithms work under the hood. It provides clear, straightforward code examples for various algorithms like deep learning networks, regression models, and clustering techniques. The input is conceptual knowledge of an algorithm, and the output is a runnable, easy-to-read Python implementation that clarifies its inner workings. This is for developers building their foundational understanding or wanting to implement algorithms from scratch for educational purposes.
10,960 stars. No commits in the last 6 months.
Use this if you are a developer who wants to learn the fundamental mechanics of machine learning algorithms by studying clean, minimal code implementations.
Not ideal if you are looking for highly optimized, production-ready machine learning libraries to use in large-scale applications.
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Python
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MIT
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Last pushed
Jun 15, 2025
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