A growing collection of machine learning algorithms implemented from scratch in Python, focused on understanding the core mathematics and logic behind each algorithm rather than relying on high-level libraries.
This repository is a personal learning project aimed at building strong fundamentals in machine learning by implementing algorithms step by step using basic Python and numerical tools.
The emphasis is on:
- clarity over performance
- understanding over abstraction
- learning how things work under the hood
- Implement core machine learning algorithms from first principles
- Understand optimization techniques like Gradient Descent
- Visualize learning behavior through plots and loss curves
- Build intuition around convergence, scaling, and model behavior
- Maintain a clean, modular, and extensible codebase