Named Entity Recognition with spaCy for Beginners

Extracting key details like names, dates, and locations from raw text is a fundamental challenge in natural language processing. This text-based course guides you through the foundational concepts of Named Entity Recognition (NER) and shows you how to automate text extraction tasks. You will transition from understanding basic linguistic structures to confidently parsing complex documents with code. What you will learn: Understand the core concepts of tokenization, part-of-speech tagging, and named entities; Extract pre-trained entity categories like organizations, locations, and monetary values using spaCy; Custom-train NER models to recognize domain-specific terms relevant to your projects; Evaluate model performance using precision, recall, and modern NLP metrics; Apply entity extraction workflows to clean and structure messy textual data. The course starts with essential terminology and the mechanics of natural language processing, then guides you step-by-step through writing and refining your own extraction scripts. This course is designed for beginners, Python enthusiasts, and aspiring data analysts with no prior NLP experience. Start reading today to unlock the hidden data inside your text documents.