A comprehensive collection of artificial intelligence and machine learning algorithms implemented in Python. This repository serves as both a learning resource and a practical reference for various AI/ML techniques.
- A* Search
- Greedy Best-First Search
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Depth-Limited Search (DLS)
- Iterative Deepening DFS (IDDFS)
- Uniform Cost Search (UCS)
- Dijkstra's Algorithm
- Naive Bayes Classifier
- Object Detection System
- Tic-Tac-Toe (Minimax)
- 8-Puzzle (A*)
- N-Queens (Genetic Algorithm)
- Python 3.x
- Required packages (varies by implementation)
- Clone the repository
git clone https://github.com/Omar7001-B/AI-Search-Algorithms.git
cd AI-Search-Algorithms- Install required packages for specific implementations
# For Machine Learning implementations
pip install numpy pandas scikit-learn
# For Object Detection
pip install opencv-python tensorflowEach algorithm implementation includes:
- Detailed README explaining the algorithm
- Implementation details and complexity analysis
- Usage instructions and examples
- Requirements and dependencies
- Search Algorithms: Path finding, puzzle solving, optimization
- Machine Learning: Text classification, object detection, pattern recognition
- Games: AI opponents, puzzle solvers, optimization problems
- Clean, well-documented code
- Efficient implementations
- Practical examples and use cases
- Performance optimizations
- Search algorithms optimized for various scenarios
- ML implementations with good accuracy-speed trade-offs
- Game AI with intelligent decision making
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch
- Commit your changes
- Push to the branch
- Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Thanks to all contributors
- Inspired by classic AI/ML algorithms
- Built with modern Python practices
- GitHub: @Omar7001-B