https://github.com/awais-124/ai-algorithms
This Repository contain Artificial Intelligence Algorithms Codes in Python
https://github.com/awais-124/ai-algorithms
ai astar-algorithm classification csp deep-learning dfs-algorithm first-order-logic hill-climbing linear-regression machine-learning tic-tac-toe
Last synced: 6 months ago
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This Repository contain Artificial Intelligence Algorithms Codes in Python
- Host: GitHub
- URL: https://github.com/awais-124/ai-algorithms
- Owner: awais-124
- Created: 2025-01-31T17:11:29.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-01-31T17:40:52.000Z (8 months ago)
- Last Synced: 2025-01-31T18:30:55.571Z (8 months ago)
- Topics: ai, astar-algorithm, classification, csp, deep-learning, dfs-algorithm, first-order-logic, hill-climbing, linear-regression, machine-learning, tic-tac-toe
- Language: Jupyter Notebook
- Homepage:
- Size: 3.27 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI Algorithms Repository
This repository contains various AI algorithms implemented in Python. These implementations cover a range of AI topics, including search algorithms, constraint satisfaction problems, logic-based AI, deep learning, and game-playing strategies. The repository serves as a learning resource from an AI Lab course.
## Table of Contents
- [Algorithms Implemented](#algorithms-implemented)
- [Installation](#installation)
- [Usage](#usage)
- [Contributions](#contributions)
## Algorithms Implemented
### Search Algorithms
- **3x3 Grid Problem** (`3x3-grid-problem.ipynb`): Solving a grid-based search problem.
- **A-star Algorithm** (`a-star-usage.ipynb`, `a-star.py`): Implementation of A* search.
- **Hill Climbing** (`hill-climbing.ipynb`): Hill climbing search technique.
- **Water Jug Problem** (`water-jug-problem.py`): State-space search problem.
### Game Playing & Decision Making
- **Alpha-Beta Pruning & Minimax** (`alphabeta-minimax.ipynb`): Game playing strategy using Minimax and Alpha-Beta pruning.
- **Tic-Tac-Toe** (`tic-tac-toe.ipynb`): Implementing AI for Tic-Tac-Toe using Minimax.
### Constraint Satisfaction Problems (CSP)
- **CSP Solving** (`CSP.ipynb`): Solving constraint satisfaction problems in AI.
### Logic-Based AI
- **First-Order Logic (FOL)** (`First-Order-Logic.ipynb`): Working with logic in AI decision-making.
### Machine Learning & Deep Learning
- **Classification Metrics** (`classification-metrics.py`): Evaluating classifiers.
- **Linear Regression** (`Linear_regression.ipynb`): Simple linear regression implementation.
- **Deep Learning Basics** (`Deep-Learning.ipynb`): Introduction to deep learning.
### General AI Algorithms
- **AI Algorithms Overview** (`ai-algos.ipynb`): Collection of various AI algorithm implementations.
## Installation
1. Clone the repository:
```sh
git clone https://github.com/awais-124/ai-algorithms.git
```
*(Ensure you have Python installed on your system.)*
## Usage
Open the Jupyter notebooks using:
```sh
jupyter notebook
```
Run Python scripts using:
```sh
python script_name.py
```
## Contributions
Contributions are welcome! Feel free to submit issues or pull requests.