📚 Learning

This repository serves as a personal knowledge base, containing code implementations, exercises, and mini-projects from different online courses, tutorials, and learning resources. Each directory represents a different course or topic, where I've practiced and applied concepts to reinforce my understanding.

🛠 Technologies & Topics Covered

• Languages: Python
• Concepts: Pre-processing, ML, DL
• Frameworks/Libraries: NumPy, Pandas, Matplotlib, Seaborn, Sklearn, Pytorch
• Tools: Git

📂 Structure

The repository is organized by topic

📦 learning
├── 📂 regression
│   │── 💻 Model_Evaluation_and_Refinement_cars.ipynb
|   |── 💻 Simple_Linear_Regression_v1.ipynb
|   |── 💻 practice_model_development_laptops_jupyterlite.ipynb
│   └── 📜 README.md
├── 📂 autoencoder
│   ├── 📂 module1
│   │    ├── 💻 covered_concepts.py
│   └── 📜 README.md
└── 📜 README.md