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AI, Machine Learning & Data ScienceUdemy2024-10 Edition100% Free Video Course

Advance RAG : Vector to Graph RAG Neo4j Adaptive AutoGen RAG

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.

21 Video Lessons
12 Hours On-Demand
Created by Udemy
Uploaded Sep 2026
English
beginner to advanced
Advance RAG : Vector to Graph RAG Neo4j Adaptive AutoGen RAG
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Course Features:
12 hours on-demand video
21 complete lectures
Streamable on mobile, tablet & desktop
Self-paced curriculum with progress tracking
Direct MP4 downloads & offline video access
Verified course archives hosted on cloud infrastructure.

What You'll Master 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.

Course Curriculum21 Lectures

10 sections • 12 hours total length

Prefer offline learning? Download all 21 video lectures and project files for free.
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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

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.

Instructor

U

Udemy

Specialist in AI, Machine Learning & Data Science

Passionate educator focused on real-world practical skills, modern frameworks, and production-ready engineering practices. Delivering step-by-step masterclasses accessible to learners globally on MJ Accedemy.

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