Applied Natural Language Processing with spaCy and LLMs

Natural language processing is at the heart of modern software applications, but moving from raw text to structured, actionable insights requires robust, production-ready tools. This comprehensive text-based course guides you through spaCy, the premier library for industrial-strength NLP. You will transition from understanding basic linguistic concepts to building highly customized pipelines and leveraging modern large language models (LLMs) for complex text processing tasks. What you will learn: Understand foundational NLP concepts including tokenization, part-of-speech tagging, and dependency parsing; Build custom information extraction pipelines using named entity recognition (NER) and rule-based matching; Configure transformer-based pipelines to leverage state-of-the-art language representations; Integrate large language models seamlessly into your workflows using the spaCy-LLM package; Create custom pipeline components and extensions to tailor spaCy to your specific domain; Apply text classification techniques to categorize documents and analyze sentiment. The course begins with essential linguistic terminology and core spaCy mechanics before moving into pipeline customization and advanced LLM integrations. You will read detailed conceptual explanations, analyze clean code examples, and practice through structured written exercises. This course is designed for developers, data analysts, and technology professionals who want to build real-world text-processing applications. No prior NLP experience is required, though a basic understanding of Python is recommended. Start reading today to build production-grade natural language pipelines.