Course Outline

Introduction to Natural Language Generation (NLG)

  • What is NLG?
  • Difference between NLU and NLG
  • Applications of NLG in real-world scenarios for government

Basic NLG Techniques

  • Template-based generation
  • Statistical models for text generation
  • Introduction to machine learning in NLG for government applications

Working with NLG Models

  • Overview of NLG models (GPT, T5) for government use
  • Setting up basic models in Python for government projects
  • Generating text using pre-trained models for government communications

Challenges in NLG

  • Handling coherence and relevance in government documents
  • Common issues in text generation for government reports
  • Ethical considerations in AI-generated content for government

Hands-On with NLG Tools

  • Introduction to NLG libraries (GPT-2/3, NLTK) for government use
  • Generating text for specific use cases in government operations
  • Evaluating generated text for quality in government contexts

Evaluating NLG Models

  • Measuring fluency and coherence in generated text for government documents
  • Automated vs. human evaluation techniques for government content
  • Improving the quality of NLG outputs for government use

Future Trends in NLG

  • Emerging techniques in NLG research for government applications
  • Challenges and opportunities for future text generation in government
  • Impact of NLG on content creation and AI development for government

Summary and Next Steps

Requirements

  • Basic understanding of programming concepts
  • Familiarity with Python programming

Audience

  • Individuals new to artificial intelligence
  • Data science professionals and enthusiasts
  • Content creators interested in leveraging AI-generated text for government and other sectors
 14 Hours

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