Module 1: Python for GenAI Applications

• Python fundamentals & OOP concepts
• Data handling with NumPy & Pandas
• API integration & automation
• Python best practices for AI apps

Module 2: AI & ML Essentials (Applied Focus)

• AI vs ML vs GenAI overview
• Supervised & Unsupervised learning concepts
• Model usage vs model training
• Understanding how ML supports GenAI systems

Module 3: Deep Learning & LLM Foundations

• Neural networks & transformers
• How LLMs work internally
• Tokens, embeddings & attention
• Pre-trained models & fine-tuning overview

Module 4: Generative AI & LLM Ecosystem

• Generative AI use cases
• Closed-source vs open-source LLMs
• GPT, LLaMA, Mistral, Gemini overview
• Choosing the right LLM for applications

Module 5: Prompt Engineering (Advanced)

• Prompt design principles
• Zero-shot, few-shot & CoT prompting
• Role-based & system prompts
• Prompt evaluation & optimization
• Guardrails & safety prompts

Module 6: LLM APIs & Frameworks

• OpenAI API implementation
• Hugging Face models & pipelines
• LangChain framework
• LlamaIndex fundamentals
• Tool calling & function calling
  

Module 7: Vector Databases & Embeddings

• Embeddings & semantic search
• FAISS, Pinecone, ChromaDB
• Indexing documents & data
• Building AI-powered search systems

Module 8: Retrieval-Augmented Generation (RAG)

• RAG architecture & workflow
• Document ingestion pipelines
• Enterprise knowledge chatbots
• Performance tuning & evaluation

Module 9: AI Agents & Automation

• Introduction to AI agents
• Agent frameworks & tools
• Multi-step reasoning workflows
• AI-powered automation use cases

Module 10: AI Application Development & Deployment

• Building GenAI-powered applications
• Chatbots, copilots & assistants
• API-based backend integration
• Streamlit / FastAPI basics
• Deployment & scalability concepts

Module 11: Capstone Project & Industry Use Cases

• End-to-end LLM application project
• Real-world business problem solving
• Project documentation & demo
• GitHub portfolio creation