Discover NLP with spaCy: tokenization, preprocessing, pipelines, training, evaluation, and deployment. Perfect for all skill levels.

jupyter.tar.gz

jupyter

spacy-code

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.

Mastering spaCy

Let's look at the solution to the previous exercise.

Getting Started

Core Operations with spaCy

Linguistic Features

Rule-Based Matchmaking

Working with Word Vectors and Semantic Similarity

Putting Everything Together: Semantic Parsing with spaCy

Assessment: spaCy Features

Customizing spaCy Models

Text Classification with spaCy

spaCy and Transformers

Putting Everything Together: Designing a Chatbot with spaCy

Appendix

Conclusion

Assessment - Machine Learning with spaCy

Solution: Word Vectors and Semantic Similarity

Solution