September 2024
Intermediate
2h 36m
English
Closed Captioning available in English
Overview
In this 2-hour course, you'll learn the fundamentals of knowledge graphs and their integration into Retrieval-Augmented Generation (RAG) systems to enhance artificial intelligence processes. The course covers practical implementation using Neo4j, focusing on concepts like vector indexing and embeddings. By the end, you'll be confident in building and optimizing Graph RAG systems that innovate AI applications.
What I will be able to do after this course
- Master the process of building and managing knowledge graphs with practical tools.
- Understand and utilize graph databases such as Neo4j effectively.
- Learn to configure development environments and use APIs like OpenAI for AI integration.
- Gain expertise in vector indexing and embedding creation for data retrieval.
- Apply knowledge graph techniques to enhance AI applications in real-world scenarios.
Course Instructor(s)
Paulo Dichone is an experienced AI developer and instructor renowned for his expertise in knowledge graphs and advanced data systems. With years of experience integrating cutting-edge technologies into practical applications, he brings a hands-on approach to teaching. Paulo is passionate about sharing his in-depth knowledge, ensuring learners acquire both theoretical insight and practical experience.
Who is it for?
This course is tailored for data scientists, AI developers, and technical professionals aiming to expand their skillset in knowledge graphs and AI integration. Participants are expected to have familiarity with Python and basic machine learning concepts, with some prior experience in databases and APIs being advantageous. It's ideal for those seeking to enhance their technical proficiency in managing and applying knowledge graphs.
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