This repository contains a curated collection of Python-based sample codes and projects focused on Data Science, Machine Learning, and Artificial Intelligence. It is designed to serve as a practical resource for learning, experimentation, and building real-world applications.
The content ranges from fundamental concepts to more advanced implementations, helping users progressively enhance their skills.
- Provide hands-on examples for core concepts in Data Science, ML, and AI
- Demonstrate real-world problem-solving using Python
- Build a strong foundation through practical implementation
- Serve as a reference for learning and revision
The repository is organised into different sections based on topics:
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA)
- Data visualisation
- Supervised learning (Regression, Classification)
- Unsupervised learning (Clustering, Dimensionality Reduction)
- Model evaluation and optimization
- Basic AI concepts and implementations
- Introductory deep learning projects
- AI-driven problem-solving examples
- End-to-end real-world projects
- Practical applications combining multiple concepts
- Python
- NumPy
- Pandas
- Matplotlib / Seaborn
- Scikit-learn
- TensorFlow / PyTorch (for deep learning projects)