Natural Language Processing (NLP) with Python spaCy Training Course
This instructor-led training session, available online or in-person, is designed for developers and data scientists looking to leverage spaCy for processing extensive text volumes to uncover patterns and extract valuable insights.
Upon completing this training, participants will be capable of:
- Installing and setting up spaCy.
- Gaining a thorough understanding of spaCy's methodology in Natural Language Processing (NLP).
- Identifying patterns and deriving business insights from large-scale data sources.
- Integrating the spaCy library into current web and legacy systems.
- Deploying spaCy in live production environments to predict human behavior.
- Utilizing spaCy for text pre-processing in Deep Learning applications.
Course Format
- Interactive lectures and discussions.
- Abundant exercises and practical practice.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For personalized training requests, please reach out to us to coordinate.
- To learn more about spaCy, visit: https://spacy.io/
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
- Experience with Python programming.
- Fundamental knowledge of statistics.
- Familiarity with command-line operations.
Target Audience
- Developers
- Data scientists
Need help picking the right course?
uzbekistan@nobleprog.com or +919818060888
Natural Language Processing (NLP) with Python spaCy Training Course - Enquiry
Natural Language Processing (NLP) with Python spaCy - Consultancy Enquiry
Testimonials (2)
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Course - Scaling Data Analysis with Python and Dask
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
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