Natural Language Processing with SpaCy Training

Overview

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 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.

 

Natural Language Processing with SpaCy Training

Objective

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

 

 

Outline:

Introduction To SpaCy And Part Of Speech Tagging
Introduction
How To Access Your Working Files

Getting Started With SpaCy
Getting Started With SpaCy Part – 1
Getting Started With SpaCy Part – 2
SpaCy Annotations
The SpaCy Data Model

Part Of Speech Tagging With SpaCy
Part of Speech Tagging With SpaCy
Training Your Own Tagger

Conclusion

 

Natural Language Processing with SpaCy Training

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