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Advanced Machine Learning Practicals

This repository contains practical implementations of advanced machine learning algorithms using Python and Jupyter Notebooks.
It is designed for students, researchers, and practitioners who want to understand the theory and hands-on coding behind common ML models.

📂 Repository Structure

  • FFNN_classification.ipynb – Feedforward Neural Network (FFNN) applied to a classification task
  • FFNN_regression.ipynb – FFNN applied to a regression task
  • Gaussian_Mixture_Model.ipynb – Gaussian Mixture Model for clustering and density estimation
  • NaiveBayes.ipynb – Implementation of Naive Bayes classifier
  • Gen & Discriminative 2.ipynb – Comparison of Generative vs. Discriminative classifiers
  • Clustering_gmm.csv – Example dataset used in Gaussian Mixture Model notebook

📘 Topics Covered

  • Feedforward Neural Networks (classification & regression)
  • Gaussian Mixture Models (clustering, density estimation)
  • Naive Bayes classifier
  • Generative vs. Discriminative models
  • Data preprocessing and visualization

📊 Example Outputs

  • Classification reports and confusion matrices
  • Regression error metrics (MSE, RMSE, MAE)
  • Cluster visualizations and probability densities
  • Training and validation performance curves

🔮 Future Work

  • Add support for more algorithms (SVM, Random Forest, Gradient Boosting)
  • Extend deep learning examples (CNNs, RNNs, Transformers)
  • Include more real-world datasets and experiments
  • Improve visualization (ROC curves, learning curves, error analysis)
  • Add unit tests for reproducibility and reliability

🤝 Contributing

Contributions are welcome!
If you’d like to add new notebooks, fix bugs, or improve documentation:

  1. Fork the repo
  2. Create a new branch (git checkout -b feature-name)
  3. Commit your changes (git commit -m "Add new feature")
  4. Push to your branch (git push origin feature-name)
  5. Open a Pull Request

👤 Author

Shaikh Musab
🔗 GitHub Profile