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Course Outline
Introduction
- Defining "Industrial-Strength Natural Language Processing"
Installing spaCy
spaCy Components
- Part-of-speech tagger
- Named entity recognizer
- Dependency parser
Overview of spaCy Features and Syntax
Understanding spaCy Modeling
- Statistical modeling and prediction
Using the SpaCy Command Line Interface (CLI)
- Basic commands
Creating a Simple Application to Predict Behavior
Training a New Statistical Model
- Data (for training)
- Labels (tags, named entities, etc.)
Loading the Model
- Shuffling and looping
Saving the Model
Providing Feedback to the Model
- Error gradient
Updating the Model
- Updating the entity recognizer
- Extracting tokens with rule-based matcher
Developing a Generalized Theory for Expected Outcomes
Case Study
- Distinguishing Product Names from Company Names
Refining the Training Data
- Selecting representative data
- Setting the dropout rate
Other Training Styles
- Passing raw texts
- Passing dictionaries of annotations
Using spaCy to Pre-process Text for Deep Learning
Integrating spaCy with Legacy Applications
Testing and Debugging the spaCy Model
- The importance of iteration
Deploying the Model to Production
Monitoring and Adjusting the Model
Troubleshooting
Summary and Conclusion
Requirements
- Python programming experience.
- A basic understanding of statistics
- Experience with the command line
Audience
- Developers
- Data scientists
14 Hours
Testimonials (3)
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
