• Learning Path
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• AI

This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. Planned content includes: - Introduction to Knowledge Graphs - Knowledge Graph Modeling & Databases - Knowledge Graph Algorithms & Reasoning - Machine Learning on Knowledge Graphs - Knowledge Graphs for Generative AI

Knowledge graphs organize entities and relationships to power intelligent systems with structured knowledge. They enhance AI applications by providing factual grounding, enabling semantic reasoning, and improving machine learning performance. This learning path covers foundational concepts, schema design and modeling, graph algorithms, machine learning techniques, and real-world applications with generative AI. You'll learn to design and query knowledge graphs, build embeddings, implement graph neural networks, and integrate knowledge graphs with large language models for retrieval-augmented generation systems. The path uses industry tools including Neo4j, SPARQL, and modern Python libraries for hands-on learning.