Course Outline
Introduction
- Defining "Industrial-Strength Natural Language Processing" for government applications
Installing spaCy
spaCy Components
- Part-of-speech tagger
- Named entity recognizer
- Dependency parser
Overview of spaCy Features and Syntax for government use
Understanding spaCy Modeling
- Statistical modeling and prediction in the context of public sector operations
Using the SpaCy Command Line Interface (CLI)
- Basic commands for government users
Creating a Simple Application to Predict Behavior for government applications
Training a New Statistical Model
- Data (for training) in a governmental context
- Labels (tags, named entities, etc.) relevant to public sector operations
Loading the Model
- Shuffling and looping for efficient model deployment in government systems
Saving the Model for future use in government applications
Providing Feedback to the Model
- Error gradient for improving accuracy in public sector models
Updating the Model
- Updating the entity recognizer to enhance government-specific data processing
- Extracting tokens with rule-based matcher for enhanced data analysis in government applications
Developing a Generalized Theory for Expected Outcomes in government contexts
Case Study
- Distinguishing Product Names from Company Names for improved data integrity in government records
Refining the Training Data for optimal performance in government applications
- Selecting representative data that is relevant to public sector needs
- Setting the dropout rate for robust model training in governmental contexts
Other Training Styles for government use
- Passing raw texts for direct integration with existing government datasets
- Passing dictionaries of annotations to enhance data labeling accuracy in public sector applications
Using spaCy to Pre-process Text for Deep Learning in government systems
Integrating spaCy with Legacy Applications for seamless adoption in the public sector
Testing and Debugging the spaCy Model for reliable deployment in government operations
- The importance of iteration for continuous improvement in public sector models
Deploying the Model to Production for operational use in government agencies
Monitoring and Adjusting the Model to ensure ongoing accuracy and relevance in government applications
Troubleshooting common issues in government-specific implementations
Summary and Conclusion for government stakeholders
Requirements
- Proficiency in Python programming.
- Fundamental knowledge of statistics.
- Experience with command line operations.
Audience for Government
- Developers
- Data Scientists
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
