Mastering spaCy, Second Edition is your comprehensive guide to building sophisticated NLP applications using the spaCy ecosystem. This revised edition builds on the expertise of Duygu Altinok, a seasoned NLP engineer and spaCy contributor, and introduces new chapters by Déborah Mesquita, a data science educator and consultant known for making complex concepts accessible. This edition embraces the latest advancements in NLP, featuring chapters on large language models with spacy-llm, transformer integration, and end-to-end workflow management with Weasel. You’ll learn how to enhance NLP tasks using LLMs, streamline workflows using Weasel, and integrate spaCy with third-party libraries like Streamlit, FastAPI, and DVC. From training custom Named Entity Recognition (NER) pipelines to categorizing emotions in Reddit posts, this book covers advanced topics such as text classification and coreference resolution. Starting with the fundamentals—tokenization, NER, and dependency parsing—you’ll explore more advanced topics like creating custom components, training domain-specific models, and building scalable NLP workflows. Through practical examples, clear explanations, tips, and tricks, this book will equip you to build robust NLP pipelines and seamlessly integrate them into web applications for end-to-end solutions.