Entity and Relationship Extraction with LLMs and Python

Unstructured text holds vast amounts of valuable information, but extracting meaningful insights manually is slow and inefficient. Learning to automatically identify key entities and their connections is the first step toward building intelligent search systems and semantic knowledge bases. This text-only course guides you through the foundations of information extraction using Large Language Models (LLMs) and Python. You will transition from analyzing raw text to parsing complex relationships and structuring data for modern knowledge graphs. What you'll learn: Understand the foundational concepts of entities, relationships, and schema design for data representation; Configure LLM prompts to extract structured JSON data reliably from unstructured text; Apply Python type hints and schema validation to ensure high-quality extraction; Build automated pipelines to parse text and map connections between key data points; Design basic knowledge graph structures to organize and store extracted relationships; Practice cleaning and resolving duplicate entities to maintain data integrity. You will start with core definitions and structural concepts before moving into hands-on Python extraction techniques, prompt engineering, and validation workflows. This course is designed for beginners in data processing, software development, or analytics, with no prior AI experience required. Start reading today to unlock the hidden relationships within your text data.