Udemy – Advance RAG : Vector to Graph RAG Neo4j Adaptive AutoGen RAG 2024-10

Apr 18, 2025 - 16:38

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Advance RAG course: Vector to Graph RAG Neo4j Adaptive AutoGen RAG. In this course, you will learn how to master Retrieval Augmented Generation (RAG), an advanced artificial intelligence technique that combines retrieval-based methods with generative models. This course is designed for developers, data scientists, AI enthusiasts, quality engineers, students who want to build practical applications using RAG, from a simple chatbot based on vector RAG to advanced chatbot with Graph RAG and self-reflective RAG. You will explore the theoretical principles, practical implementation and real-world use cases of RAG. At the end of this course, you will have the skills to create RAG-based AI applications.

What you will learn in this course

Basics of RAG (Retrieval Augmented Generation) and NLP: Understanding the core concepts to build a strong foundation of NLP and RAG.
Understanding of NLP process like markup, embedding, POS, TF-IDF, segmentation etc.
Understanding the evaluation of NLP models from the rule-based model to the transformer model.
Understanding the transformer model with a simple RAG example.
Setting up the environment for the practical implementation of the RAG program using Python and VS Code
Learn to build a vector based RAG application with Streamlit chatbot, langchain and vectordb.
Learn advanced RAG technique with Graph RAG, LLM and Streamlit chatbot. Learn how to configure Neo4j, create Graph RAG, display graph in your chatbot.
Advanced RAG learning with hybrid search technique using Graph RAG. Self-reflexive RAG learning with Langgraph. Practical use cases with RAG Python code.
RAG re-ranking with cohere API to improve RAG retrieval process.
Practical uses in RAG.
Tests to check learning.
Building an agent-based RAG program with Autogen. Agent-oriented RAG.

This course is suitable for people who:

Data scientists
Machine learning engineers
Artificial intelligence and natural language processing enthusiasts
Software developers and engineers
Researchers and academics
Product managers and technical supervisors
Students and learners
Artificial intelligence experts and consultants
Quality engineers

Advance RAG course specifications: Vector to Graph RAG Neo4j Adaptive AutoGen RAG

Publisher: Udemy
Lecturer: Soumen Kumar Mondal
Training level: beginner to advanced
Training duration: 2 hours and 43 minutes
Number of courses: 22

Course headings

Advance RAG course prerequisites: Vector to Graph RAG Neo4j Adaptive AutoGen RAG

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.