Project Title
ML-From-Scratch — Bare Bones NumPy Implementations of Machine Learning Models
Overview
ML-From-Scratch is an open-source Python project that provides transparent and accessible NumPy implementations of fundamental machine learning models and algorithms. Unlike other projects that focus on computational efficiency, ML-From-Scratch prioritizes clarity and understanding, making it an excellent resource for learning and educational purposes.
Key Features
- Comprehensive coverage from linear regression to deep learning
- Transparent and accessible code implementations
- Focus on educational value over computational efficiency
Use Cases
- Educators and students learning the fundamentals of machine learning
- Developers seeking to understand the inner workings of ML algorithms
- Researchers needing a baseline for developing and testing new ideas
Advantages
- Transparent code for better understanding of ML algorithms
- Wide range of algorithms covered, from basic to advanced
- Open-source and community-driven, allowing for continuous improvement
Limitations / Considerations
- Not optimized for performance, making it less suitable for production environments
- Implementations are from scratch, which might not be the most efficient in terms of code or computation
Similar / Related Projects
- TensorFlow: A powerful and efficient open-source software library for machine learning. It is more production-oriented and less focused on educational transparency.
- Scikit-learn: A simple and efficient tool for data mining and data analysis. It provides a high-level API for various machine learning tasks but does not offer the same level of transparency into the algorithms' inner workings.
Basic Information
- GitHub: https://github.com/eriklindernoren/ML-From-Scratch
- Stars: 27,342
- License: Unknown
- Last Commit: 2025-08-20
📊 Project Information
- Project Name: ML-From-Scratch
- GitHub URL: https://github.com/eriklindernoren/ML-From-Scratch
- Programming Language: Python
- ⭐ Stars: 27,342
- 🍴 Forks: 4,781
- 📅 Created: 2017-02-05
- 🔄 Last Updated: 2025-08-20
🏷️ Project Topics
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