AI Algorithms — Basic Algorithms for Learning

A beginner-friendly collection of basic AI and machine learning algorithms for learning. This repository contains hands-on Jupyter notebooks in Python that implement classic search techniques, game-playing AI, probabilistic models, evolutionary algorithms, and introductory ML — ideal for students, self-learners, and anyone building a foundation in artificial intelligence.

Purpose: Basic algorithms for learning — not production libraries. Run each notebook cell-by-cell to understand how each algorithm works under the hood.

---

Table of Contents

• What You Will Learn
• Topics Covered
• Repository Structure
• Prerequisites
• Getting Started
• Notebooks Guide
• Contributing
• Author

---

What You Will Learn

This repo helps you learn foundational AI algorithms through practical Python code:

• Graph search — BFS, DFS, UCS, GBFS, and A* pathfinding
• Local search — Hill climbing and simulated annealing
• Constraint satisfaction — N-Queens (backtracking and genetic algorithms)
• Adversarial search — Minimax for Tic-Tac-Toe
• Probabilistic AI — Hidden Markov Models (HMM)
• Machine learning — Supervised and unsupervised learning with scikit-learn
• Neural networks — From-scratch forward pass and sigmoid activation

---

Topics Covered

Category	Algorithms & Concepts
Uninformed Search	Breadth-First Search (BFS), Depth-First Search (DFS), Uniform Cost Search (UCS)
Informed Search	Greedy Best-First Search (GBFS), A* Algorithm
Local Search	Hill Climbing, Simulated Annealing
CSP / Optimization	N-Queens Problem, Genetic Algorithms
Game AI	Minimax Algorithm, Tic-Tac-Toe AI
Probabilistic Models	Hidden Markov Model (HMM), Viterbi-style inference
Machine Learning	Decision Trees, Random Forest, Naive Bayes, K-Means, PCA
Neural Networks	Perceptron-style layers, weights, biases, sigmoid activation

---

Repository Structure

AI-Algorithms/
├── bfs_and_dfs.ipynb              # BFS & DFS graph traversal
├── UCS.ipynb                      # Uniform Cost Search
├── GBFS.ipynb                     # Greedy Best-First Search
├── A_star_algorithm.ipynb         # A* pathfinding
├── Local Searching Algorithms  .ipynb   # Hill climbing & simulated annealing
├── n-queen_problem.ipynb        # N-Queens (backtracking)
├── genetic_algorithm .ipynb       # Genetic algorithm basics
├── Genetic Algorithm implementation on N-Queen.ipynb
├── minimax_tic_tac_toe.ipynb      # Minimax game AI
├── HMM.ipynb                      # Hidden Markov Models
├── supervised ML with sklearn.ipynb
├── Unsupervised_Learning.ipynb
├── Neural Networks.ipynb
├── titanic.csv                    # Sample dataset (Titanic)
└── README.md

---

Prerequisites

• Python 3.8+
• Jupyter Notebook or JupyterLab
• Familiarity with basic Python and NumPy is helpful

Install Dependencies

pip install jupyter numpy pandas matplotlib scikit-learn

---

Getting Started

1. Clone the repository

   git clone https://github.com/danishjavedcodes/AI-Algorithms.git
   cd AI-Algorithms

2. Launch Jupyter

   jupyter notebook

3. Open a notebook from the list above and run cells in order.

4. Experiment — change graph weights, heuristics, board sizes, or hyperparameters to see how each algorithm behaves.

---

Notebooks Guide

Notebook	Description
bfs_and_dfs.ipynb	Implement and compare BFS and DFS on a sample graph
UCS.ipynb	Find lowest-cost paths with Uniform Cost Search
GBFS.ipynb	Greedy Best-First Search on Romania-style map graphs
A_star_algorithm.ipynb	A* search with admissible heuristics
Local Searching Algorithms .ipynb	Hill climbing and simulated annealing for optimization
n-queen_problem.ipynb	Solve N-Queens using backtracking
genetic_algorithm .ipynb	Core GA: selection, crossover, mutation
Genetic Algorithm implementation on N-Queen.ipynb	Apply GA to the N-Queens problem
minimax_tic_tac_toe.ipynb	Play Tic-Tac-Toe against a minimax AI
HMM.ipynb	Hidden Markov Model — states, transitions, observations
supervised ML with sklearn.ipynb	Classification on Titanic data (Decision Tree, RF, Naive Bayes)
Unsupervised_Learning.ipynb	K-Means clustering and PCA on Titanic data
Neural Networks.ipynb	Build a simple neural network forward pass from scratch

---

Keywords

artificial intelligence, AI algorithms, machine learning, search algorithms, A star, BFS, DFS, genetic algorithm, minimax, hidden markov model, neural networks, scikit-learn, python, jupyter notebook, learn AI, basic algorithms, beginner AI

---

Contributing

Contributions are welcome. If you find a bug, want to add a notebook, or improve explanations:

1. Fork the repository
2. Create a feature branch (git checkout -b improve/readme-or-notebook)
3. Commit your changes
4. Open a Pull Request

---

Author

danishjavedcodes

• GitHub: https://github.com/danishjavedcodes/AI-Algorithms

---

License

This project is open source and intended for educational use. Feel free to use, study, and share these notebooks for learning basic AI algorithms.