🌐 Machine Learning & Deep Learning Toolkit

This repository contains various templates and examples for machine learning and deep learning projects. It is organized into several directories, each focusing on different aspects and techniques of ML & DL. Most of these resources were necessary while tackling the Machine Learning & Deep Learning A-Z course and were required for proper understanding and implementation.

📁 Project Structure

Deep Learning/
    Machine Learning basics/
        Classification Template/
        Data Preprocessing Template/
    Supervised Deep Learning/
        ANN/
        CNN/
        RNN/
    Unsupervised Deep Learning/
        AutoEncoders/
        Boltzmann Machines/
        GANs/
        SOM (Self Organizing Maps)/
license
Machine Learning/
    clean.py
    Templates/
        1. Regression/
        2. Classification/
        3. Clustering/
        4. Association Rule Learning/
        5. Reinforcement Learning/
        6. Deep Learning/
        7. NLP/
        A. Model Selection/
Training/
README.md

📝 Directories and Files

Deep Learning

• Machine Learning Basics: Contains templates for fundamental ML tasks such as classification and data preprocessing.
• Supervised Deep Learning: Includes examples and templates for ANN, CNN, and RNN models.
• Unsupervised Deep Learning: Covers techniques such as AutoEncoders, Boltzmann Machines, GANs, and SOMs.

Machine Learning

• clean.py: A script to remove code cells from Jupyter notebooks, making them shareable without exposing code.
• Templates: Includes templates for major ML tasks:
  • Regression
  • Classification
  • Clustering
  • Association Rule Learning
  • Reinforcement Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Model Selection

Training

• Contains datasets and training scripts used in various ML & DL tasks.

💪 Usage

Cleaning Jupyter Notebooks

Use the clean.py script to strip code cells from Jupyter notebooks:

python Machine Learning/clean.py

Running Templates

Each template contains scripts and notebooks that can be executed to perform specific ML/DL tasks. Navigate to the required template directory and follow the instructions in the README or notebooks.

📜 License

This project is licensed under the MIT License. See the license file for details.

Attribution

If you use this project, please mention GitHub Copilot in your documentation or credits.

🤝 Contributing

Contributions are welcome! Fork the repository and submit a pull request with your enhancements.

Acknowledgements

This repository includes data and examples from multiple sources. Check individual directories and files for specific references and credits.

📞 Contact

For questions or suggestions, open an issue in the repository or reach out to the maintainers.