Introducing spaCy: The Industry-Standard NLP Library This comprehensive course will equip you with the necessary skills to effectively utilize spaCy, a rapidly growing industry-standard library, for a wide range of natural language processing tasks. From tokenization and sentence segmentation to parsing and named entity recognition, spaCy offers powerful and user-friendly features that are ready for production use. Master the Core Operations of spaCy Begin by gaining a deep understanding of spaCy's core operations and how they can be leveraged to parse text and extract valuable insights from unstructured data. Explore the various classes within spaCy, including Doc, Span, and Token, and discover how to utilize different components to calculate word vectors and predict semantic similarity. Train spaCy Models and Harness the Power of Pattern Matching Develop your skills in crafting both simple and complex matching patterns to extract specific terms and phrases from unstructured data using spaCy's EntityRuler, Matcher, and PhraseMatcher. Additionally, learn how to create custom pipeline components and generate training and evaluation data. Delve into the process of training spaCy models and gain proficiency in utilizing them for inference. Throughout the course, you will work on real-world examples to solidify your understanding and apply spaCy effectively in your own NLP projects.