Description
This complete Generative AI course takes you from beginner to advanced with hands-on projects, real-world applications, and career-ready skills. You’ll learn the foundations of Generative AI, explore Large Language Models (LLMs), master frameworks like LangChain, LlamaIndex, CrewAI, and PydanticAI, and deploy your own AI solutions on the cloud. The course is tailored to equip you with both the knowledge and practical experience required to step into a Generative AI Engineer role.
Each section includes quizzes & coding exercises to help you test your knowledge and reinforce your skills.
What you’ll learn in each section
1. Introduction – Get started with the course, understand what you will learn & set up Python environments (Colab, Jupyter, PyCharm).
2. Generative AI – Foundation – Understand AI vs ML vs DL vs GenAI, dive into Large Language Models, and learn the Transformer architecture.
3. Accessing LLMs in Python – Use OpenAI, Gemini, Groq, and Ollama LLMs, and connect them through LangChain and LlamaIndex.
4. Prompt Engineering – Explore prompt templates, zero-shot, and few-shot prompting to effectively interact with LLMs.
5. Building GenAI Chatbots – Build and deploy chatbots step by step using LangChain, LlamaIndex, Streamlit UI, and Streamlit Cloud.
6. Retrieval-Augmented Generation (RAG) – Understand RAG, build RAG pipelines with LangChain and LlamaIndex, and create a PDF Q&A bot.
7. AI Agents – Learn what AI agents are and build agents with PydanticAI, AutoGen, and CrewAI for multi-agent workflows.
8. LLM Deployment – Deploy open-source LLMs with Ollama, Docker, and vLLM, and set them up on AWS EC2 for real-world usage.
9. Model Context Protocol (MCP) – Understand MCP, build an MCP server, and integrate MCP tools with PydanticAI and CrewAI agents.
10. Capstone Projects – Apply everything learned to build real-world AI projects: Enterprise Chatbots, RAG Assistants, and Intelligent AI Agents with Full Cloud Deployment.
