A curated collection of applied machine learning projects focused on solving real-world problems through data, experimentation, and intelligent systems.
This repository documents my practical work and continued development across machine learning, data science, and artificial intelligence.
- Supervised and unsupervised learning
- Classification and regression
- Feature engineering and data preprocessing
- Model training, validation, and optimization
- Predictive analytics
- Data visualization and exploratory data analysis
- Deep learning
- Computer vision
- Real-world AI problem solving
Languages
- Python
Machine Learning & Data
- Scikit-learn
- XGBoost
- Pandas
- NumPy
Deep Learning
- PyTorch
- TensorFlow
Visualization
- Matplotlib
Development & Experimentation
- Jupyter Notebook
- Google Colab
- Git & GitHub
Each project is organized independently and may contain:
project-name/
├── README.md
├── data/
├── notebooks/
├── src/
├── models/
├── results/
└── requirements.txt