Learn the essentials of problem-solving with spaCy, the popular, open-source software library for advanced natural language processing.
Overview
Syllabus
Introduction
- Why use spaCy?
- Prerequisites of the course
- How to install 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
- spaCy's data structures
- Similarity and word vectors
- Integrating spaCy's models and rules
- Challenge: Phrase matching
- Solution: Phrase matching
- 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
- 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
- Wrap-up
Taught by
Prateek Sawhney