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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 (5)
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
It was a though course as we had to cover a lot in a short time frame. Our trainer knew a lot about the subject and delivered the content to address our requirements. It was lots of content to learn but our trainer was helpful and encouraging. He answered all our questions with good detail and we feel that we learned a lot. Exercises were well prepared and tasks were tailored accordingly to our needs. I enjoyed this course
Bozena Stansfield - New College Durham
Course - Build REST APIs with Python and Flask
Trainer develops training based on participant's pace
Farris Chua
Course - Data Analysis in Python using Pandas and Numpy
As I was the only participant the training could be adapted to my needs.
