Project Title

homemade-machine-learning — Python implementations of popular machine learning algorithms with interactive Jupyter demos and math explanations

Overview

The homemade-machine-learning project provides a comprehensive set of Python examples for popular machine learning algorithms, complete with interactive Jupyter Notebook demos and detailed explanations of the underlying mathematics. Unlike many other resources, this project focuses on implementing algorithms from scratch, offering a deeper understanding of the mechanics and math behind each method.

Key Features

Implementation of machine learning algorithms in Python
Interactive Jupyter Notebook demos for hands-on learning
Detailed explanations of the mathematics behind each algorithm
Focus on understanding rather than using third-party library one-liners

Use Cases

Educators and students looking to understand the fundamentals of machine learning
Developers seeking to implement machine learning algorithms without relying on third-party libraries
Researchers needing a deeper understanding of the mathematical principles of machine learning

Advantages

Enhances understanding of machine learning algorithms through hands-on implementation
Provides a practical approach to learning with interactive Jupyter Notebooks
Offers a solid foundation in the mathematics behind machine learning techniques

Limitations / Considerations

Not intended for production use, as implementations are "homemade" and not optimized
May require a strong background in mathematics and Python to fully benefit from the project
The project is focused on educational purposes rather than practical applications

Scikit-learn: A widely-used machine learning library in Python that offers efficient tools for data mining and data analysis. It differs from homemade-machine-learning in that it provides production-ready implementations.
TensorFlow: An end-to-end open-source platform for machine learning. It offers a more comprehensive and production-oriented approach compared to homemade-machine-learning.
Keras: A high-level neural networks API, capable of running on top of TensorFlow, CNTK, or Theano. It is more focused on ease of use and production applications than homemade-machine-learning.

Basic Information

GitHub: https://github.com/trekhleb/homemade-machine-learning
Stars: 23,654
License: Unknown
Last Commit: 2025-08-20

📊 Project Information

Project Name: homemade-machine-learning
GitHub URL: https://github.com/trekhleb/homemade-machine-learning
Programming Language: Jupyter Notebook
⭐ Stars: 23,654
🍴 Forks: 4,096
📅 Created: 2018-11-01
🔄 Last Updated: 2025-08-20

🏷️ Project Topics

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🎮 Online Demos

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homemade-machine-learning

Project Description