Building NLP Pipelines with spaCy

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

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