Natural Language Processing with spaCy: Entities and Dependency Parsing

Extracting meaningful insights from unstructured text is a critical skill in modern data analysis and software development. This course introduces you to the foundational concepts of Natural Language Processing using spaCy, a powerful and industry-standard Python library designed for production-ready text processing. You will transition from understanding raw text data to building structured pipelines that automatically identify key entities and map the grammatical relationships within sentences. The course incorporates modern Python standards, including type hints and clean package management, to ensure your code is robust and maintainable. What you'll learn: - Understand foundational NLP concepts, tokenization, and linguistic annotations. - Extract names, dates, organizations, and custom categories using Named Entity Recognition. - Analyze sentence structures and grammatical relationships with Dependency Parsing. - Configure pipeline components and optimize spaCy models for text analysis tasks. - Apply modern Python development practices, including type hints, to your NLP scripts. - Practice parsing real-world text datasets to extract structured information. We begin with core terminology and essential NLP concepts before moving step-by-step into practical text processing, pipeline configuration, and structural analysis. This text-based program is designed specifically for beginners who want to learn through clear, written explanations and code examples. This course is perfect for software developers, data analysts, and tech professionals looking to start their journey in natural language processing with no prior NLP experience required.