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Duration 14 hours
Course Outline
Natural Language Generation (NLG) Fundamentals
- Defining NLG and its scope
- Distinguishing between NLU and NLG
- Real-world applications of NLG
Foundational NLG Approaches
- Template-driven generation methods
- Statistical frameworks for creating text
- The role of machine learning in NLG
Implementing NLG Models
- Overview of key models such as GPT and T5
- Configuring basic models within Python
- Generating text via pre-trained architectures
Addressing NLG Challenges
- Ensuring coherence and contextual relevance
- Common pitfalls in automated text creation
- Ethical aspects of AI-produced content
Practical NLG Tool Integration
- Overview of libraries like GPT-2/3 and NLTK
- Applying text generation to specific scenarios
- Assessing the quality of generated output
Assessing NLG Performance
- Evaluating fluency and logical flow
- Comparing automated versus human assessment methods
- Strategies for enhancing output quality
Future Directions in NLG
- Advanced techniques in current research
- Upcoming challenges and opportunities in text synthesis
- The influence of NLG on content creation and AI progress
Conclusion and Recommended Pathways
Requirements
- Foundational knowledge of programming logic
- Basic proficiency in Python
Target Learners
- Individuals new to AI
- Data science professionals and enthusiasts
- Content creators exploring AI-assisted writing
