AI Enhancement with Knowledge Graphs - Mastering RAG Systems: Unleashing the Power of Knowledge Graphs in RAG Systems

Paulo Dichone

€8.99 ~~€67.99~~

Video Sep 2024 2hrs 36mins 1st Edition

Paulo Dichone

€8.99 ~~€67.99~~

Video Sep 2024 2hrs 36mins 1st Edition

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Key benefits

In-depth knowledge graph training to master their structure and use in AI applications
Hands-on Neo4j experience to build, manage, and query complex knowledge graphs effectively
Advanced RAG integration techniques to enhance AI models with vector indexing and data retrieval

Description

Unlock the potential of knowledge graphs and RAG systems in this comprehensive course. Begin by setting up your development environment and configuring tools like the OpenAI API. You'll explore fundamental concepts of knowledge graphs, their structure, and their critical role in data systems. These early modules provide a strong foundation, ensuring you're ready for hands-on applications and more advanced topics. Moving forward, you’ll dive into practical implementation using Neo4j, a popular graph database. You’ll learn to construct and manage knowledge graphs, create entities and relationships, and execute complex queries to extract meaningful insights. Each lesson is designed to build your skills step-by-step, offering real-world scenarios to deepen your understanding. By the end of this section, you’ll be equipped to handle knowledge graphs effectively in diverse projects. The course then focuses on integrating knowledge graphs with RAG systems to enhance AI applications. You’ll master techniques such as vector indexing and embedding creation for efficient data retrieval. The final project brings everything together, enabling you to build a complete GraphRAG system. This course offers a thorough journey into a cutting-edge area of AI and data science, equipping you with the expertise to innovate and lead in your field.

Who is this book for?

This course is ideal for data scientists, AI developers, and technical professionals who are familiar with Python and have basic knowledge of machine learning concepts. Prior experience with databases and APIs is recommended but not required.