Essentials and Applications of Artificial Intelligence

Overview

This repository provides a structured collection of fundamental concepts and practical applications in Artificial Intelligence (AI). It is designed to support learning by combining theoretical knowledge with relevant real-world use cases.

Trashnet [Kaggle] ==> https://www.kaggle.com/datasets/feyzazkefe/trashnet

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Objectives

• Develop a clear understanding of core AI concepts
• Organise key topics in a structured format
• Bridge the gap between theory and practical application

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Topics Covered

The repository includes a range of essential AI topics, such as:

• Artificial Intelligence fundamentals
• Intelligent agents and environments
• Search and problem-solving techniques
• Machine Learning principles
• Data preprocessing and analysis
• Evaluation metrics (e.g., accuracy, precision, recall, F1-score)

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WEB APP In Action

Ngrok Setup

Landing Page

About Page

Team Member Page

User Guidance Page

Image Testing

[1] Glass Image Testing

[2] Metal Image Testing

[3] Plastic Image Testing

[4] Paper Image Testing

[5] General Trash Testing

[6] Human Image Testing

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Applications

The concepts explored in this repository are applied in areas including:

• Computer Vision
• Data analysis and classification
• AI-based web applications

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Technologies

• Python
• Flask
• NumPy and Pandas
• PyTorch
• Jupyter Notebook
• Ngrok

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Future Work

• Expansion of practical implementations such as (integrating with IOT devices)

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License

This project is open-source and available under the MIT License.