This repository serves as cold storage for my complete Machine Learning journey—covering concepts, notes, implementations, and experiments. It is structured for easy reference, continuous learning, and reproducibility.
- Document concepts and methods systematically
- Maintain a searchable reference for revision and reuse
- Apply ML methods to practical scenarios and track outcomes
- Support long-term mastery through organized experimentation
- Python
- NumPy, Pandas, Matplotlib, Seaborn
- scikit-learn
- TensorFlow, Keras, PyTorch (where applicable)
Feel free to browse, fork, or clone for learning or experimentation. Contributions via issues or pull requests are welcome.