Explore every machine learning model in exhaustive detail—from hyperparameter tuning and training mechanics, through parameter optimization, to the final prediction calculation. Perfect for developers and researchers seeking crystal-clear, step-by-step implementations of core ML algorithms with code, math, and intuition.
- Detailed tutorials on individual ML models
- Code walkthroughs illustrating training processes
- Hyperparameter tuning explained and demonstrated
- Parameter optimization strategies
- Stepwise calculation of predictions with math and code
- Clear explanations combining theory with practical examples
- Python 3.8 or higher
- Install dependencies:
pip install -r requirements.txt
Clone the repository:
git clone https://github.com/yourusername/ExplainableML.git
cd ExplainableML
pip install -e .
- Explore notebooks for interactive learning
- Run scripts in
src/to see models in action - Use provided tools to tune hyperparameters and optimize performance
Contributions are welcome! Please open issues and submit pull requests with improvements or new models.
MIT License
Made for anyone passionate about truly understanding how machine learning models work — not just running them.