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Machine Learning Coursework

ML tutorials and implementations covering core algorithms and techniques.

📁 Structure

Folder Contents
01-Fundamentals/ ML basics, model evaluation
02-Supervised-Learning/ Decision Trees, KNN, Linear & Logistic Regression, SVM
03-Unsupervised-Learning/ K-Means, PCA
04-NLP/ Text processing, NLP, email classification, text vectorization
05-Recommender-Systems/ Collaborative filtering
07-Feature-Engineering-and-Pipelines/ Feature engineering, ML pipelines
08-Model-Deployment-and-MLOps/ Model deployment, FastAPI, Django, and MLOps principles
datasets/ Sample datasets (cancer, heart disease, titanic, wine)
Lectures/ Course materials

🚀 Quick Start

git clone https://github.com/RK0297/Machine-Learning-Coursework.git
pip install jupyter numpy pandas scikit-learn matplotlib seaborn tensorflow
jupyter notebook

📖 Requirements

Python 3.7+ • Jupyter • NumPy • Pandas • Scikit-learn • TensorFlow

💡 Notes

  • Each notebook is self-contained with explanations and comments
  • Datasets are included for immediate hands-on practice
  • Some notebooks include AI-generated code examples for reference
  • Models are saved in .h5 format for reproduction

Author: Radhakrishna Bharuka

Last Updated: May 2026