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Course Outline
Introduction
- Comparison of Spark NLP, NLTK, and spaCy
- Overview of Spark NLP features and architecture for government
Getting Started
- Setup requirements for government
- Installing Spark NLP in a government environment
- General concepts for effective use in public sector operations
Using Pre-trained Pipelines
- Importing required modules for government applications
- Utilizing default annotators in government workflows
- Loading a pipeline model for government tasks
- Transforming texts for government analysis
Building NLP Pipelines
- Understanding the pipeline API for government use
- Implementing Named Entity Recognition (NER) models for government projects
- Choosing appropriate embeddings for government data
- Utilizing word, sentence, and universal embeddings in government applications
Classification and Inference
- Document classification use cases for government operations
- Sentiment analysis models tailored for government communications
- Training a document classifier for government documents
- Integrating other machine learning frameworks in government projects
- Managing NLP models for government compliance and efficiency
- Optimizing models for low-latency inference in government systems
Troubleshooting
Summary and Next Steps
Requirements
- Proficiency with Apache Spark for government applications
- Experience in Python programming
Audience
- Data Scientists
- Software Developers
14 Hours
Testimonials (5)
The live examples
Ahmet Bolat - Accenture Industrial SS
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very interactive...
Richard Langford
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Sufficient hands on, trainer is knowledgable
Chris Tan
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Get to learn spark streaming , databricks and aws redshift
Lim Meng Tee - Jobstreet.com Shared Services Sdn. Bhd.
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practice tasks