A structured, lesson-based learning platform for data scientists diving into the world of Artificial Intelligence and Large Language Models (LLMs). Each lesson includes theory, visual explanations (sketchnotes), hands-on experiments, and curated resources.

• 📚 Structured Lessons: Progressive learning path from beginner to advanced
• 🎨 Visual Sketchnotes: Simplified visual explanations for complex concepts
• 🧪 Hands-On Experiments: Interactive demos and applications to practice concepts
• 📖 Curated Resources: Carefully selected articles, videos, tools, and books
• 🎯 Learning Objectives: Clear goals for each lesson
• 📱 Responsive Design: Works seamlessly on desktop, tablet, and mobile devices
• 🚀 No Build Process: Pure HTML, CSS, and JavaScript - open and use immediately
• ♿ Accessible: Keyboard navigation and screen reader friendly

1. 🤖 Introduction to AI (45 min) - What is AI, types, and applications
2. 🧠 Machine Learning Basics (60 min) - Supervised, unsupervised, and reinforcement learning
3. 🔬 Deep Learning (75 min) - Neural networks, CNNs, RNNs, and modern architectures

4. 🔤 Large Language Models (90 min) - Transformers, training techniques, and applications
5. ✍️ Prompt Engineering (60 min) - Crafting effective prompts for LLMs
6. 🛠️ AI Frameworks & Tools (75 min) - PyTorch, TensorFlow, Hugging Face, and more

7. 🔍 RAG Systems (90 min) - Building retrieval-augmented generation applications
8. ✅ Best Practices & Ethics (60 min) - Safety, ethics, and production deployment

• 📖 Key Terminology - Essential AI and LLM concepts
• 🌟 Popular Models - GPT, Claude, Gemini, LLaMA, BERT, and more
• 💡 Applications - Real-world use cases and implementations

Simply open index.html in your web browser. No installation or build process required!

If you want to run a local server:

# Using Python 3
python -m http.server 8080

# Using Python 2
python -m SimpleHTTPServer 8080

# Using Node.js
npx http-server -p 8080

Then navigate to http://localhost:8080 in your browser.

Each lesson includes five key sections:

1. 📖 Overview - Introduction and learning objectives
2. 🎨 Sketchnote - Visual summary of concepts (simplified drawings)
3. 📚 Core Content - Detailed explanations with examples
4. 🧪 Experiments - Hands-on demos and applications
5. 📚 Resources - Articles, videos, tools, and books for deeper learning

Want to add your own lessons or experiments?

1. Check out the LESSON_GUIDE.md for detailed instructions
2. Use lessons/intro-to-ai.html as a template
3. Add your sketchnotes to /assets/sketchnotes/
4. Link your AI experiments/demos in the experiments section
5. Update index.html to include your new lesson

Experiments are hands-on demonstrations that help learners understand concepts:

• Host on GitHub Pages, CodePen, Streamlit, or Hugging Face Spaces
• Link them in the "Experiments" section of relevant lessons
• Each experiment should demonstrate a core concept from the lesson

• Learning: Self-paced learning resource for AI/ML newcomers
• Reference: Quick lookup for terminology and concepts
• Teaching: Educational material for instructors and mentors
• Onboarding: Help new team members understand AI/LLM fundamentals

Contributions are welcome! Feel free to:

• Add new content sections
• Improve existing explanations
• Fix typos or errors
• Enhance the UI/UX
• Add new features

This project is licensed under the MIT License - see the LICENSE file for details.

Built to help data scientists and developers understand the rapidly evolving world of AI and Large Language Models.