Knowledge Graphs for RAG

DeepLearning.AI is a specialist online education platform founded in 2017 that focuses on practical, industry-relevant training in artificial intelligence and machine learning. Designed for learners at multiple levels, its courses emphasize hands-on projects, clear conceptual foundations and tools commonly used in industry. The platform’s flexible online format makes it accessible to professionals and students worldwide who want to build skills without relocating.

Courses and specializations are structured to help learners develop applied portfolios, with real-world assignments, code notebooks and community review. Collaboration with leading practitioners ensures content remains current with industry practice, and certificate programs help demonstrate competencies to employers. The platform also supports career transitions, offering guidance on interviewing for technical roles and connecting learners with opportunities to showcase their work.

For international learners seeking concentrated, practice-oriented AI education, DeepLearning.AI provides a clear route to upskill quickly and build demonstrable expertise. Its global learner community, modular course design and emphasis on project-based learning make it a pragmatic choice for those aiming to enter research, product or engineering roles in the AI ecosystem.

This program is a non-degree course in Knowledge Graphs for RAG, taught in English, and is suitable for students with a pre-high school education level. It helps students structure complex data relationships and drive intelligent search functionality. The program runs for several weeks and is offered online by DeepLearning.AI in California, United States.

The curriculum covers the basics of knowledge graphs, including nodes and edges, and how to use Neo4j's query language, Cypher. Students will learn to add vector indexes to knowledge graphs, build graphs from scratch, and explore advanced techniques for connecting multiple graphs. They will also practice writing advanced Cypher queries to retrieve relevant information.

After completing this program, students can pursue careers as Data Analysts, AI Engineers, Information Architects, or Data Scientists. They can work in industries such as technology, finance, or healthcare, and with employers like research institutions or tech companies. Students will be well-equipped to uncover deeper insights in their data and enhance the performance of large language models.