A structured repository documenting my journey of learning Machine Learning — from classical algorithms.
This repository is focused on learning, experimentation, notes, and problem solving.
Real-world implementations and end-to-end projects are maintained in a separate repository.
- Build strong ML fundamentals
- Understand algorithms mathematically and intuitively
- Practice with small coding exercises
- Maintain well-organized notes
- Prepare for real-world ML projects
- Build a strong foundation for MLOps
- ✅ Classical Machine Learning
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- scikit-learn
This repository is intended for learning and experimentation only.
Large-scale projects, APIs, deployments, and production-ready implementations are maintained in a separate repository.