🤖 AI Algorithms in Python

This repository is a collection of fundamental AI algorithms implemented from scratch in Python. The goal is to help students, researchers, and enthusiasts understand the inner workings of classic search and machine learning algorithms without relying heavily on external libraries.

📚 Contents

The project covers both search-based AI algorithms and machine learning techniques.

🔍 Search & Game Algorithms

Depth First Search (DFS) & Uniform Cost Search (UCS)

A* Search Algorithm

Beam Search & Simulated Annealing

Sudoku Solver

Tic Tac Toe (Minimax)

Connect4 (Minimax with Alpha-Beta Pruning)

🧬 Optimization & Learning Algorithms

Genetic Algorithm

k-Nearest Neighbors (KNN)

ID3 Decision Tree Algorithm

K-Means Clustering

Neural Networks (basic implementation)

⚡ Features

Implemented from scratch in Python for clarity.

Uses Jupyter Notebooks for interactive learning.

Minimal dependencies (only numpy and matplotlib for some algorithms).

Covers classic AI, optimization, and ML concepts.

🛠 Installation & Setup

Clone this repository:

git clone cd AI-Algorithms-Python

Install dependencies:

pip install numpy matplotlib

Launch Jupyter Notebook:

jupyter notebook

Open any .ipynb file and start experimenting 🚀

🎯 Purpose

This project is designed for:

Students – to learn and visualize core AI concepts.

Researchers – as a quick reference for classic algorithms.

Enthusiasts – to build intuition by exploring algorithms step by step.

📌 Notes

Each algorithm is implemented in a self-contained notebook.

Code is written with a focus on readability and educational clarity.

This project is not optimized for production use but for learning and experimentation.