Get in Touch

Course Outline

Introduction to Natural Language Generation (NLG)

  • Defining NLG
  • Distinguishing between NLU and NLG
  • Real-world applications of NLG

Core NLG Methodologies

  • Template-based text generation
  • Statistical models for text production
  • Intro to machine learning within NLG

Utilizing NLG Models

  • Overview of key NLG models (GPT, T5)
  • Configuring basic models in Python
  • Text generation using pre-trained models

Challenges in NLG

  • Ensuring coherence and relevance
  • Addressing common text generation issues
  • Ethical aspects of AI-generated content

Practical Application of NLG Tools

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

Model Evaluation

  • Measuring fluency and coherence in output
  • Comparing automated vs. human evaluation methods
  • Enhancing the quality of NLG results

Future Directions in NLG

  • Novel techniques in NLG research
  • Future challenges and opportunities in text generation
  • The influence of NLG on content creation and AI advancement

Conclusion and Recommended Next Steps

Requirements

  • Fundamental knowledge of programming concepts
  • Basic proficiency in Python programming

Target Audience

  • Beginners in AI
  • Data science enthusiasts
  • Content creators exploring AI-generated text
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories