Master Advanced RAG:HandsOn Chatbot from Vector to Graph RAG

Unlock the Power of Generative AI with RAG: Dive Deep into Vector RAG, Graph RAG, Self-Reflective RAG with Hands-On


What you'll learn
Fundamentals of RAG (Retrieval-Augmented Generation) and NLP: Understand core concepts to build strong foundation of NLP and RAG.
Understand process of NLP like tokenization, embedding, POS, TF-IDF, chunking etc.
Understand evaluation of NLP models from Rule based to Transformer model.
Understand transformer model with simple RAG example.
Environment setup for hands on implementation of RAG application using Python and VS Code
Learn to build vector based RAG application with Streamlit chatbot, langchain and vectordb.
Learn advance RAG technique with Graph RAG , LLM and Streamlit chatbot. Learn how to setup Neo4j, create Graph RAG , show graph in your chatbot.
Learn advance RAG with hybrid search technique using Graph RAG. Learn self reflective RAG with Langgraph. Practical use cases with python code of RAG.
Practical use cases on RAG.
Quizzes to check learning.

Requirements
No prior RAG experience required.
Very basic python knowledge will help.
Don't worry without python knowledge also you will learn how to implement RAG chatbot.

Description
In this course, you will learn how to master Retrieval-Augmented Generation (RAG), a cutting-edge AI technique that combines retrieval-based methods with generative models. This course is designed for developers, data scientists, and AI enthusiasts, quality engineers, Students who want to build practical applications using RAG, ranging from simple vector RAG chatbot to advanced chatbot with Graph RAG and Self Reflective RAG. You'll explore the theoretical foundations, practical implementations, and real-world use cases of RAG. By the end of this course, you will have the skills to create RAG-based AI applications.
After completing the course, you will be able to create chatbot with multiple RAG techniques using Streamlit, LangChain, LangGraph, Groq API and many more. Along with that you will also learn fundamentals and concepts.
Course Objectives
Understand the fundamental concepts of RAG and NLP.
Understand concepts of NLP with examples like tokenization, chunking, TF-IDF, embedding.
Understand evaluation of NLP models from rule based to transformer model.
Understand transformer model and components with examples.
Environment setup for hands on implementation.
Build first chatbot with Streamlit and Langchain.
Build a vector RAG chatbot with Groq API.
Understand Graph RAG and implement Graph RAG with Neo4j.
Understand Self Reflective or Adaptive RAG and implement with LangGraph.
Real world use cases of RAG.
Check your understanding with Quizzes.
Lets deep dive into world of RAG to understand it.
Who this course is for:
Data Scientists
Machine Learning Engineers
AI and NLP Enthusiasts
Developers and Software Engineers
Researchers and Academics
Product Managers and Technical Leads
Students and Learners
AI Practitioners and Consultants
Quality Engineers

More Info