AI Projects - Beginner-Friendly Learning Repository

Welcome! This repository contains 5 beginner-friendly AI projects designed to help you learn and build AI applications. Each project includes simple Python templates, clear comments, detailed README files, step-by-step guides, and minimal dependencies.

🎯 Purpose

This repository prioritizes clarity over complexity. Each project is designed to:

• Be easy to understand for beginners
• Teach core AI concepts through hands-on practice
• Use minimal dependencies
• Include extensive comments and documentation
• Provide practical, real-world examples

📚 Projects

1. RAG App (Retrieval Augmented Generation)

Location: projects/1-rag-app/

Build an AI that answers questions using your own documents. Learn how to combine document retrieval with AI generation.

What you'll learn:

• Document processing and chunking
• Embeddings and vector search
• Retrieval Augmented Generation (RAG)
• Working with OpenAI API

2. Social Media AI Agent with Approval

Location: projects/2-social-media-agent/

Create an AI assistant that generates social media posts with human approval. Learn about AI-human collaboration and ethical AI use.

What you'll learn:

• AI content generation
• Approval workflows
• Content moderation
• Ethical AI principles

3. Stock Data AI Assistant

Location: projects/3-stock-assistant/

Build an AI that explains stock market data in simple terms. Combines real-time data with AI explanations.

What you'll learn:

• Working with financial APIs
• Real-time data processing
• AI-powered explanations
• Data interpretation

4. AI with Memory

Location: projects/4-ai-memory/

Create a conversational AI that remembers previous interactions across sessions. Learn how to build personalized AI experiences.

What you'll learn:

• Conversation memory management
• Context preservation
• Session persistence
• Personalized AI responses

5. Data Quality Copilot

Location: projects/5-data-quality-copilot/

Build an AI that analyzes CSV files and identifies data quality issues. Learn about data analysis and quality assessment.

What you'll learn:

• Data quality metrics
• CSV file processing
• AI-powered data analysis
• Automated reporting

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• Basic Python knowledge
• OpenAI API key (Get one here)

Quick Start

1. Clone the repository

   git clone https://github.com/nerdjerry/ai-projects.git
   cd ai-projects

2. Choose a project

   cd projects/1-rag-app  # Or any other project

3. Install dependencies

   pip install -r requirements.txt

4. Set up your API key Create a .env file:

   OPENAI_API_KEY=your-api-key-here
   

5. Run the project

   python rag_app.py  # Or the main file for your chosen project

📖 Learning Path

Recommended order for beginners:

1. Start with Project 4 (AI with Memory) - Simplest to understand conversational AI
2. Then try Project 2 (Social Media Agent) - Learn about content generation and approval
3. Move to Project 1 (RAG App) - Understand document-based AI
4. Try Project 3 (Stock Assistant) - Work with external data sources
5. Finish with Project 5 (Data Quality Copilot) - Apply AI to data analysis

💡 Tips for Success

• Read the README first - Each project has detailed documentation
• Start small - Run the examples before modifying code
• Experiment - Try different inputs and see what happens
• Read the comments - Code is heavily documented to help you learn
• Ask questions - Use the issues tab if you get stuck

🛠️ Common Setup

All projects use:

• OpenAI API for AI capabilities
• python-dotenv for environment variables
• Minimal additional dependencies - Each project lists its specific requirements

📝 Project Structure

Each project follows this structure:

project-name/
├── README.md           # Detailed project documentation
├── <project_main>.py   # Main Python file (descriptively named, e.g., rag_app.py)
├── requirements.txt    # Python dependencies
├── .env.example        # Example environment variables
└── data/              # Sample data or documents (if applicable)

🤝 Contributing

Contributions are welcome! Please:

• Keep the beginner-friendly focus
• Maintain clear documentation
• Add comments to explain your code
• Test your changes

📄 License

This project is open source and available for educational purposes.

⚠️ Important Notes

• These projects are for educational purposes
• API calls to OpenAI will incur costs (typically minimal for learning)
• Always review AI-generated content
• Keep your API keys secure (never commit them to git)

🌟 Next Steps

After completing these projects, you can:

• Build your own AI applications
• Combine concepts from multiple projects
• Explore more advanced AI topics
• Share your creations with the community

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Happy Learning! 🎓

If you find these projects helpful, please star ⭐ this repository!