Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
17 Hour(s) 53 Minute(s)
Language
English
Taught by
Siddhardhan S
Rating
4 views
Course Overview
Learn Generative AI from scratch – Build RAG, AI Agents & Chatbots, master MCP, and deploy real-world projects
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.
Course Content
- 10 section(s)
- 62 lecture(s)
- Section 1 Introduction
- Section 2 Generative AI – Foundation
- Section 3 Accessing LLMs in Python
- Section 4 Prompt Engineering
- Section 5 Building Generative AI Chatbots
- Section 6 RAG - Retrieval-Augmented Generation
- Section 7 AI Agents
- Section 8 LLM Deployment
- Section 9 MCP – Model Context Protocol
- Section 10 Capstone Projects – Build and Deploy Real-World AI Solutions
What You’ll Learn
- Master the foundations of Generative AI, Large Language Models, and Transformer architecture.
- Build real-world AI applications including chatbots, RAG systems, MCP servers, and multi-agent systems.
- Deploy LLM-powered solutions on the cloud using Docker, Streamlit, Ollama, vLLM, and AWS EC2.
- Gain the knowledge and hands-on skills required to step into a Generative AI Engineer role.
Reviews
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UUtkarsh sharma
The explanation provides only high-level knowledge without demonstrating a deep understanding of the underlying concepts. Additionally, the code does not follow proper standards, which could be misleading for beginners.
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SSANJAY MAHARJAN
As of now Everything that is covered its totally understood
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AAyush Dubey
Just completed section5, kind of disappointed. So far till now, i wish instead of just showing what to code you could focus MORE on the WHY part, which would help in building the intuition behind these things. why this certain function was used here, when do we use it, what does it do. Your focus is more on the what part, however, people that are beginners and wanna learn how to build, for them when and why is more crucial. Your ML course is such a gem. It was long, but still, by far the best ML course I've ever seen. There you took your time and explained each line and the WHY and WHEN behind it. You missed that kind of here. I will agree that now i know a few stuffs about GenAI, but i still will have to refer other resources to understand it in-depth. Kind of disappointed from this course so far, cant say for the remaining part. ill still complete it by next week and will let you know my overall feedback. I hope your upcoming DL course wouldn't be like this, i really wish you make it as lengthy as needed, core of the concept is much more important than just knowing how code lines work. It's the core concept which builds intuition and it takes time.
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RRajeshwari Nataraj
Very appropriate for me. very clear explanations.
