A structured personal repository for reviewing and practicing essential Machine Learning (ML) concepts.
This repo is organized into thematic folders, each with its own README.md and learning materials, including theoretical markdown notes and implementation notebooks.
| Folder | Description |
|---|---|
basic/ |
Core ML foundations: loss functions, activation, regularization, generalization, etc. |
model/ |
Fundamental ML models: linear regression, logistic regression, MLP, etc. |
Each subfolder contains:
- 📘 Markdown theory notes
- 💻 Python (NumPy / PyTorch) implementations
- 📈 Visualizations where applicable
🛠️ This repository is continuously evolving as part of my ML learning and interview prep journey.