Intermediate

Apply graph algorithms at scale using the Python GDS client and Aura Graph Analytics

Learn to apply graph algorithms to real-world business problems

Continue your learning journey with Cypher queries

Make your graph more performant with Cypher constraints and indexes

Use Neo4j temporal types to filter, compute, and search date-based data

Create and query full-text indexes for case-insensitive search

Define and enforce a graph schema using graph types in Neo4j

Learn how to import CSV data into Neo4j using Cypher

Learn the fundamentals of Neo4j Graph Data Science

Extract structured communication metadata from documents and build entity networks in Neo4j

Learn how Neo4j and GraphRAG can support your Generative AI projects

Understand and search unstructured data using vector indexes

Learn how to use Generative AI and LLMs to convert unstructured data into knowledge graphs.

Build an AI agent that records its reasoning, then query the trace to understand what it did and why

Learn how to use the Neo4j MCP server and tools to create intelligent AI applications

Build your own GraphRAG MCP server with graph-backed tools and resources.

Build your own GraphRAG MCP server with graph-backed tools and resources using TypeScript.

Learn to build and publish agentic systems in Neo4j Aura

Learn how to interact with Neo4j from Python using the Neo4j Python Driver

Learn how to interact with Neo4j using the Neo4j Java Driver

Learn how to interact with Neo4j from Go using the Neo4j Go Driver

Learn how to interact with Neo4j using the Neo4j .NET Driver

Learn how to interact with Neo4j in your TypeScript project using the Neo4j JavaScript Driver

Learn how to use the Spring Data Neo4j library to interact with Neo4j

Learn how to create GraphQL APIs using Neo4j GraphQL Toolbox and Library.

Learn how to backup, restore and monitor Neo4j Aura instances in production

Run Neo4j in Docker, then scale up to a fault-tolerant cluster