This directory contains code implementations of the Machine Learning concepts covered in the book.
It includes models, algorithms, and mathematical functions, allowing readers to experiment with real implementations.
These code snippets are meant to be interactive supplements to the book, helping bridge theory and practice.
✅ Machine Learning models coded from scratch.
✅ Neural Networks implementations (without deep learning frameworks).
✅ Reinforcement Learning agents demonstrating AI behavior.
✅ Mathematical implementations of core AI concepts.
✅ Code snippets for practical experimentation and modification.
Files are organized by topic, making it easy to navigate.
- Browse the available folders to find relevant code.
- Download or copy snippets for experimentation.
- Some implementations may include README.md files with further explanations.
These scripts are designed to supplement the book with hands-on applications of the concepts discussed.
This directory will be expanded with additional implementations over time, covering:
- Advanced Machine Learning techniques.
- More neural network architectures.
- Reinforcement learning strategies.
If you find errors or have improvements, feel free to open an Issue or submit a Pull Request!
This project is designed to grow with new contributions and refinements.
💡 Happy coding! 🚀