This course offers a complete journey through Neo4j, starting with graph database fundamentals and the property graph model. You will master Cypher, Neo4j’s powerful query language, through hands-on labs covering filtering, aggregation, and advanced queries like MERGE and shortest path. Next, you’ll explore the Graph Data Science (GDS) library, applying algorithms such as centrality, community detection, and node similarity to uncover deep insights in complex networks. The course also introduces GraphQL integration to build flexible APIs that simplify querying and mutating graph data. In advanced modules, you will build knowledge graphs from unstructured data using Large Language Models (LLMs), supported by Python scripting and cloud tools like Google Colab. You’ll learn to set up Neo4j environments, install key plugins, and optimize query performance for production use. Finally, you’ll explore emerging AI trends with Retrieval-Augmented Generation (RAG) and GraphRAG techniques, enabling intelligent retrieval and content generation powered by knowledge graphs. This comprehensive approach combines theory, practical labs, and real-world use cases to prepare you for leveraging Neo4j in modern data and AI-driven applications.