🎓 AI Foundation Workshop - Interactive Notebooks

Welcome to the AI Foundation Workshop! This hands-on learning experience will teach you how to build real AI applications using Python and modern AI models.

Regional pricing is available for eligible learners, with discounts of up to 60% in supported regions. Start here: https://www.learnwithparam.com/ai-bootcamp

🌟 What You'll Learn

Through 6 interactive Jupyter notebooks, you'll master:

1. 💬 Text Generation - Build conversational AI and chatbots
2. 🖼️ Multimodal AI - Work with images and vision models
3. 📊 Structured Outputs - Extract reliable data from text
4. 🔧 Function Calling - Give AI access to custom tools
5. 🔌 Model Context Protocol (MCP) - Connect AI to standardized services
6. 🧠 Reasoning Models - Solve complex problems step-by-step

🚀 Quick Start

Prerequisites

• Python 3.10+
• uv (will be installed automatically if using make setup)
• API key (Google AI Studio recommended for beginners)

Fast Track (5 minutes)

1. Get your API key: Google AI Studio
2. Setup & Run:

   cd ai-foundation-workshop
   
   # One command to setup .env, install dependencies, and run!
   make dev

3. Configure: The first time you run it, it will create .env. Edit .env and add your API key, then run make dev again.
4. Start Learning: Open 01-text-generation.ipynb at http://localhost:8888

📚 Notebook Guide

Recommended Learning Path

Follow the notebooks in order - each builds on concepts from previous ones:

🌍 Universal Topics (Work with Any AI Provider)

Notebook	Topic	Time	What You'll Build
01	Text Generation	45 min	Restaurant chatbot with personality
02	Multimodal AI	45 min	Menu description generator from photos
03	Structured Outputs	60 min	Resume parser and invoice processor
04	Function Calling	60 min	Calculator agent and weather bot
05	MCP	60 min	Travel assistant with real-time data
06	Reasoning Models	45 min	Math tutor and code debugger

🎯 Learning Approach

Each notebook follows a consistent, beginner-friendly structure:

📖 Scenario-Based Learning

Every concept is introduced through real-world scenarios you can relate to.

Example: Instead of "learn about streaming," you'll build a chatbot where streaming makes responses feel instant.

🛠️ Hands-On Practice

• Working code examples you can run immediately
• Discussion questions to deepen understanding
• Challenge tasks to test your skills
• Real-world applications you can build

💡 Key Insights

Look for these throughout each notebook:

• 💡 Key Insight - Important concepts explained simply
• ❓ Discussion Question - Think critically about what you learned
• 🎯 Challenge Task - Apply your knowledge
• ⚠️ Important Note - Critical information to remember

🔑 API Keys & Models

Supported Providers

The notebooks work with multiple AI providers:

Provider	Models	Cost	Best For
Google (Gemini)	gemini-2.0-flash-exp	Free tier!	Beginners, all notebooks
OpenAI	gpt-4o, gpt-4o-mini	Paid	Production apps
Anthropic	claude-3-5-sonnet	Paid	Advanced reasoning
OpenRouter	Multiple models	Varies	Model comparison

Getting API Keys

• Google AI Studio (Recommended for beginners): https://aistudio.google.com/apikey
• OpenAI: https://platform.openai.com/api-keys
• Anthropic: https://console.anthropic.com/

Model Requirements

• Notebooks 1, 3-6: Any model works
• Notebook 2 (Multimodal): Requires vision-enabled model
  • ✅ gemini/gemini-2.5-flash
  • ✅ gpt-4o
  • ✅ claude-3-5-sonnet-20241022

💻 Running the Notebooks

We provide a Makefile to make common tasks easy. Run make help to see all commands.

Option 1: Local Development (Recommended)

This automatically checks for uv (installing it if needed), creates a virtual environment, installs dependencies, and starts JupyterLab.

# 1. Setup & Install
make dev

Note: The first time you run this, it will create .env. You must edit .env and add your API key before the notebooks will work!

Manual Steps (if you prefer):

make setup      # Create .env and install uv
make install    # Install dependencies (uv sync)
make run        # Start JupyterLab (uv run jupyter lab)

Option 2: Docker (Isolated)

Perfect for keeping your system clean or ensuring consistency.

make setup      # Create .env (Edit to add API key)
make build      # Build image
make up         # Start container

• Access: http://localhost:8888
• Stop: make down
• Logs: make logs

Option 3: Google Colab (Cloud)

1. Upload notebook to Google Drive
2. Open with Google Colab
3. Recommended: Use Colab Secrets for API keys:
   • Click the 🔑 key icon in the left sidebar
   • Add a new secret: GOOGLE_API_KEY
   • Paste your API key as the value
   • Uncomment the Colab secrets lines in the notebook:

     from google.colab import userdata
     os.environ["GOOGLE_API_KEY"] = userdata.get('GOOGLE_API_KEY')

Option 4: VS Code

1. Install the Jupyter extension
2. Open the notebook
3. Select Kernel -> Python Environments
4. Run cells with the play button

🎓 Learning Tips

For Complete Beginners

1. Start with Notebook 1 - Don't skip ahead!
2. Read the scenarios - They make concepts relatable
3. Run every code cell - Learning by doing is key
4. Try the challenges - They solidify your understanding
5. Ask questions - Use discussion questions to think deeper

For Experienced Developers

1. Skim the basics - But don't skip the "Key Insights"
2. Focus on challenges - Test your understanding
3. Experiment - Try different models and parameters
4. Build real apps - Use the "Real-World Applications" as starting points

Common Issues

"Module not found" error

make install

"RuntimeWarning: coroutine was never awaited" (Colab/Jupyter) This is already fixed in the notebooks! Each notebook includes a cell that enables async support. If you still see this error:

import nest_asyncio
nest_asyncio.apply()

"API key not found" error

• Check your .env file exists
• Verify the key is correct
• Make sure you're using the right key name (GOOGLE_API_KEY, etc.)

"Model not found" error

• Check your DEFAULT_MODEL in .env
• Verify you have the right API key for that model
• Try gemini/gemini-2.5-flash (free tier)

🏗️ What You Can Build

After completing these notebooks, you'll be able to build:

Beginner Projects

• 💬 Customer support chatbot
• 📧 Email auto-responder
• 📝 Content summarizer
• 🖼️ Image description generator

Intermediate Projects

• 🔍 Research assistant with web search
• 📊 Data extraction pipeline
• 🧮 Math tutoring bot
• 🐛 Code review assistant

Advanced Projects

• 🤖 Multi-tool AI agent
• 📈 Business intelligence dashboard
• 🎯 Decision support system
• 🔬 Scientific research assistant

📖 Additional Resources

Documentation

• LiteLLM Docs - Multi-provider AI library
• Google AI Studio - Test Gemini models
• OpenAI Docs - GPT models
• Anthropic Docs - Claude models
• OpenRouter Docs - OpenRouter models

Community

• Share your projects and ask questions
• Connect with other learners
• Get help when stuck

Next Steps

After completing these notebooks:

1. Build a real project using what you learned
2. Explore advanced topics (RAG, agents, fine-tuning)
3. Deploy your AI application to production
4. Share your learnings with others!

🤝 Contributing

Found a typo? Have a suggestion? Want to add an example?

• Open an issue
• Submit a pull request
• Share your feedback

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🎉 Ready to Start?

1. ✅ make setup
2. ✅ make dev
3. ✅ Open 01-text-generation.ipynb
4. ✅ Start building!

Happy learning! 🚀

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Questions? Stuck on something? Remember: Every expert was once a beginner. Take your time, experiment, and enjoy the journey!