SpaCy is an accessible tool that newcomers to the field of natural language processing (NLP) can use to accomplish large scale information extraction tasks.
In this course, which is designed for the intermediate level Python programmer, data scientist Aaron Kramer covers the challenges common to NLP, shows how the spaCy library for Python can address those challenges, explains how and why part-of-speech tagging is used in NLP, and provides you with the knowledge you need to extend spaCy for functional needs like sentiment models or relation extraction.
- Learn the basics of using spaCy for common natural language processing tasks
- Explore the core spaCy data structures and the data model
- Learn how to see and intuit key workflows with spaCy
- Understand part-of-speech tagging and its value
- Learn the theory behind statistical part-of-speech tagging
- Gain experience training a spaCy part-of-speech tagger on a new dataset
Aaron Kramer is a data scientist and engineer with Los Angeles based DataScience Inc. He is a spaCY contributor who holds a BA in Economics from Swarthmore College and is the author of multiple O'Reilly titles on the subject of natural language processing.