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LinkedIn Learning

Building NLP Pipelines with spaCy

via LinkedIn Learning

Overview

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Learn the essentials of problem-solving with spaCy, the popular, open-source software library for advanced natural language processing.

Syllabus

Introduction
  • Why use spaCy?
  • Prerequisites of the course
  • How to install spaCy
1. Text Processing with spaCy
  • Introduction to spaCy
  • spaCy's statistical models
  • spaCy's containers
  • Introduction to matching based on rules
  • Challenge: Predicting linguistic annotations
  • Solution: Predicting linguistic annotations
2. Data Analysis Using spaCy
  • spaCy's data structures
  • Similarity and word vectors
  • Integrating spaCy's models and rules
  • Challenge: Phrase matching
  • Solution: Phrase matching
3. Processing Pipelines with spaCy
  • Processing pipelines
  • Pipeline's custom components
  • Extension attributes: Part 1
  • Extension attributes: Part 2
  • Performance and scaling
  • Challenge: Processing streams and selective processing
  • Solution: Processing streams and selective processing
4. Training an Artificial Neural Network
  • Training and updating models
  • Training loop
  • Challenge: Building a training loop
  • Solution: Building a training loop
  • Training loop best practices
  • Challenge: Training multiple labels
  • Solution: Training multiple labels
Conclusion
  • Wrap-up

Taught by

Prateek Sawhney

Reviews

4 rating at LinkedIn Learning based on 46 ratings

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