ML-DL-From-Scratch

Welcome to the ML-DL-From-Scratch library! This library is designed to implement fundamental and advanced machine learning algorithms from the ground up using minimal dependencies. Our goal is to provide a clear and educational resource for understanding and experimenting with machine learning.

Features

• Supervised Learning:

  • Linear Regression
  • Logistic Regression
  • Support Vector Machines (SVMs)
  • Decision Trees
  • Random Forests

• Unsupervised Learning:

  • k-Means Clustering
  • Principal Component Analysis (PCA)

• Utilities:

  • Data Preprocessing (scaling, normalization, encoding)
  • Model Evaluation Metrics (e.g., accuracy, precision, recall, RMSE)
  • Dataset Splitting (train/test/validation)

Installation

1. Clone the repository:

   git clone https://github.com/CODE-ESI/ml-dl-from-scratch.git

2. Navigate to the project directory:

   cd ml-dl-from-scratch

3. Install dependencies:

   pip install -r requirements.txt

Usage

Example: Linear Regression

Here's a simple example to use the Linear Regression module:

from ml_dl_from_scratch.supervised.line_fit import LineFit

# Example dataset
X = [[1], [2], [3], [4]]
y = [3, 7, 11, 15]

# Initialize and train the model
model = LineFit()
model.train(X, y)

# Predict
predictions = model.estimate([[5], [6]])
print(predictions)  # Output: [19, 23]

More examples are available in the /examples directory.

Directory Structure

ml-dl-from-scratch/
├── core_algorithms/          # Core ML algorithms
├── datasets/                 # Example datasets
├── utils/                    # Utility functions
├── examples/                 # Example scripts for using the library
├── tests/                    # Unit tests for validation
├── LICENSE                   # License information
├── README.md                 # Project overview
└── requirements.txt          # Required Python dependencies

Contribution

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a feature branch: git checkout -b feature-name.
3. Commit your changes: git commit -m 'Add feature-name'.
4. Push to the branch: git push origin feature-name.
5. Create a pull request.

Please ensure your code follows the repository's coding standards and includes tests where applicable.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For questions, suggestions, or collaboration, feel free to open an issue or contact us through the CODE-ESI GitHub organization.

Happy Learning!