Natural Language Processing Exercises with spaCy

Building a solid foundation in Natural Language Processing requires hands-on practice with real text data. This text-based course is designed to help you transition from theory to practice by guiding you through core NLP tasks. You will learn how to manipulate text, extract meaning, and prepare raw data for downstream machine learning models. By working through structured written exercises, you will develop a practical intuition for processing language data. You will start with foundational concepts like tokenization and part-of-speech tagging, and progress to identifying named entities and using modern word vectors for semantic similarity. What you'll learn: - Understand foundational NLP concepts and core pipeline architectures within spaCy - Practice tokenization, lemmatization, and stop-word removal on diverse text samples - Extract named entities and analyze grammatical structures using part-of-speech tagging - Configure custom pipeline components and rule-based matchers for specific text patterns - Apply modern word vectors to calculate semantic similarity between words and documents This course begins with basic terminology and core pipeline concepts before guiding you through practical, step-by-step coding exercises. Each section provides clear explanations followed by written code implementation tasks to reinforce your learning. This course is designed for beginner Python developers, aspiring data scientists, and analysts who want to build practical NLP skills. No prior experience with natural language processing is required, though basic Python familiarity is recommended. Start writing code to analyze and understand text today.