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

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