Entity Communication Networks

Welcome to the Entity Communication Networks course.

In this hands-on course, you will extract text from raw documents, parse it into structured records, and build a communication network in Neo4j. You'll handle OCR noise, build rule-based and LLM-based parsers, train an NER model, and combine them into a hybrid pipeline that imports a metadata graph of entities and their communications.

By the end, you'll also receive a prompt pack — an LLMiGo to glossary for large language model (opens in a new tab)A model trained on text to predict the next token, and so to generate language. project that examines your own data, references the techniques from this course, and builds a custom pipeline for any document source. The course teaches you the concepts; the pack applies them to your data.

Before taking this course, you should have:

• Completed the Cypher Fundamentals course, or equivalent CypheriFull definition for Cypher (opens in a new tab)Neo4j's implementation of GQL, the ISO standard query language for graph databases. It is declarative: you describe the pattern to find, and the database decides how to find it. experience
• Basic understanding of Neo4j property graphiFull definition for property graph (opens in a new tab)The data model Neo4j implements, in which nodes and relationships both carry properties as well as labels and types. concepts
• Familiarity with Python