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Duration 14 hours
Course Outline
Foundations of Natural Language Generation (NLG)
- Defining NLG
- Distinguishing between NLU and NLG
- Real-world applications of NLG
Fundamental NLG Methodologies
- Template-based text generation
- Statistical approaches to text creation
- Basics of machine learning in NLG
Implementing NLG Models
- Overview of key NLG models (GPT, T5)
- Initializing basic models in Python
- Generating text with pre-trained models
Navigating NLG Challenges
- 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
Assessing NLG Model Performance
- Evaluating fluency and coherence in output
- Comparing automated and human evaluation methods
- Enhancing the quality of NLG results
Emerging Trends in NLG
- Recent developments in NLG research
- Future challenges and opportunities in text generation
- The role of NLG in content creation and AI evolution
Conclusion and Future Pathways
Requirements
- Foundational knowledge of programming principles
- Basic proficiency in Python programming
Target Audience
- Individuals new to AI
- Data science enthusiasts
- Content creators exploring AI-generated text
