This repository contains a collection of practical recipes for implementing classic machine learning algorithms — from foundational supervised learning models to more advanced techniques.
All code is written in Python using Jupyter Notebooks, and includes:
- 🔧 Pure NumPy implementations — to help you understand the core mechanics of each algorithm without relying on external ML libraries.
- ⚙️ Scikit-learn implementations — to demonstrate how the same models can be applied efficiently using industry-standard tools.
The goal is to provide an educational resource for learners who want to understand how machine learning works under the hood.
These notebooks are ideal for:
- Beginners who want to go beyond black-box libraries
- Students looking to deepen their intuition for ML algorithms
- Anyone curious about the step-by-step math and code behind common models
Feel free to explore, modify, and use! 🚀