This course offers an extensive introduction to the widely-used Python library, spaCy, for natural language processing (NLP). It covers spaCy basics, such as tokenization and part-of-speech tagging, as well as advanced topics like custom model training and NLP pipeline creation.

The course has three parts:
Part 1 focuses on spaCy’s fundamentals, its architecture, installation, and setup. It teaches common NLP tasks like tokenization, named entity recognition (NER), part-of-speech (POS) tagging, and dependency parsing.

Part 2 delves into spaCy’s features, covering syntax and semantics. It explores pattern matching and semantics via word vectors, and provides a thorough discussion of statistical information extraction techniques.

Part 3 examines advanced topics, discussing the development of complex NLP models, requiring expertise, analysis, and practical experiences. Multiple experiments with various NLP tasks are conducted, including customizing statistical models to meet specific needs.