Mímir, Keeper of the Well of Wisdom
Complete Generative AI Course: RAG, AI Agents & Deployment
https://www.udemy.com/course/generative-ai-for-beginners-
chatbots-rag-mcp-ai-agents/
Year : 2025
Language : English
Level : All Levels
Category : Development
Subcategory : Data Science
Duration : 18h 17m
Lectures : 64
Rating : 4.5/5 (762 reviews)
Students : 5,854
INSTRUCTOR(S)
HEADLINE
Learn Generative AI from scratch ? Build RAG, AI Agents &
Chatbots, master MCP, and deploy real-world projects
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.
REQUIREMENTS
* This course requires only a basic understanding of Python
and Machine Learning. No prior knowledge of Generative AI is
needed ? we start from the fundamentals and progress to
advanced concepts. All you need is the curiosity to learn by
building real-world projects.
WHO IS THIS COURSE FOR
* This course is for students, developers, and professionals
with basic Python/ML knowledge who want to become Generative
AI Engineers through hands-on projects.
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.
COURSE CONTENT
Chapter 1: Introduction
1. Introduction
2. What you will learn
3. Environment Setup: Python, IDEs & Dev Tools
Chapter 2: Generative AI ? Foundation
4. AI vs ML vs DL vs GenAI
5. Large Language Models
6. Transformer - architecture
Chapter 3: Accessing LLMs in Python
7. OpenAI LLMs (Proprietary)
8. Gemini LLMs (Proprietary)
9. Groq LLMs (Open-Source)
10. Ollama (Open-Source & Local)
11. Accessing LLMs via LangChain
12. Accessing LLMs via LlamaIndex
13. Practice Exercise - Section 3
Chapter 4: Prompt Engineering
14. Using Prompt Template
15. Zero-shot Prompting
16. Few-shot Prompting
17. Practice Exercise - Section 4
Chapter 5: Building Generative AI Chatbots
18. Building a chatbot with LangChain
19. Building a chatbot with Llamaindex
20. GenAI Chatbot with Streamlit UI
21. Deploy GenAI Chatbot on Streamlit Cloud
22. Practice Exercise - Section 5
Chapter 6: RAG - Retrieval-Augmented Generation
23. Understanding RAG
24. Important Update: LangChain Changes for RetrievalQA
25. Building a RAG system in Python with LangChain
26. Building a RAG system in Python with Llamaindex
27. Build a PDF question-answering RAG app with Streamlit
28. Practice Exercise - Section 6
Chapter 7: AI Agents
29. Understanding AI Agents
30. Build AI Agent with PydanticAI
31. Build AI Agent with Microsoft's AutoGen
32. Multi-Agent system with CrewAI
33. Practice Exercise - Section 7
Chapter 8: LLM Deployment
34. Running LLMs Locally with Ollama & Docker
35. Launching an AWS EC2 Instance
36. Deploying Ollama LLMs on EC2 with Docker
37. vLLM - High-Performance Serving on EC2
38. Serve Local LLMs (Ollama) via FastAPI
39. Deploying LLMs on RunPod (Cost-effective GPU)
Chapter 9: MCP ? Model Context Protocol
40. Understanding MCP
41. Build an MCP Server
42. Pydantic AI Agent with MCP tool
43. CrewAI Agent with MCP tool
44. Practice Exercise - Section 9
Chapter 10: Capstone Projects ? Build and Deploy Real-World AI
Solutions
45. Section 10 - Capstone Projects - Real-World GenAI
Applications
46. Project 1 - ConvoPro ? Private ChatGPT Clone
47. Project 1 - ConvoPro - DB & Environment Setup
48. Project 1 - ConvoPro - Implementation
49. Project 1 - ConvoPro - Deploy on EC2
50. Project 2 - StudyPal ? RAG-Powered AI Study Assistant
51. Project 2 - StudyPal - Environment Setup
52. Project 2 - StudyPal - Document Ingestion
53. Project 2 - StudyPal - RAG Pipeline Implementation
54. Project 2 - StudyPal - EC2 Deployment
55. Project 3 - AstraRAG - Agentic RAG Chatbot - Production-
Grade
56. Project 3 - AstraRAG - Environment Setup
57. Project 3 - AstraRAG - Document Ingestion Pipeline
58. Project 3 - AstraRAG - Build RAG Agent
59. Project 3 - AstraRAG - Build Backend & Frontend
60. Project 3 - AstraRAG - Deploy locally with Docker
61. Project 3 - AstraRAG - EC2 Deployment with Docker
62. Conclusion
Chapter 11: Bonus
63. Agent Builder - Build Trading Assistant workflow
64. Build AI Agents with LangChain V1
DATES
Published : 2025-09-22
Last Updated : 2025-12-05
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