Course Outline

Introduction to Natural Language Generation (NLG)

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

Basic NLG Techniques

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

Working with NLG Models

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

Challenges in NLG

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

Hands-On with NLG Tools

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

Evaluating NLG Models

  • Measuring fluency and coherence in generated text
  • Automated vs. human evaluation techniques
  • Improving quality of NLG outputs

Future Trends in NLG

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

Summary and Next Steps

Requirements

  • Basic understanding of programming concepts
  • Familiarity with Python programming

Audience

  • AI beginners
  • Data science enthusiasts
  • Content creators interested in AI-generated text
 14 Hours

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  • Format: Online (live), In-company (at your offices), or Hybrid.
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