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Course Outline
Introduction to Natural Language Generation (NLG)
- Defining NLG
- Distinctions between NLU and NLG
- Real-world applications of NLG
Fundamental NLG Techniques
- Template-based generation
- Statistical models for text creation
- Introduction to machine learning in NLG
Utilizing NLG Models
- Overview of NLG models (GPT, T5)
- Setting up basic models in Python
- Generating text using pre-trained models
Challenges in NLG
- Ensuring coherence and relevance
- Common pitfalls in text generation
- Ethical considerations in AI-generated content
Practical Application 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
- Fundamental knowledge of programming principles
- Basic proficiency in Python programming
Target Audience
- Novices in AI
- Data science enthusiasts
- Content creators interested in AI-assisted writing
14 Hours
