Introduction to Natural Language Generation (NLG)

• Basic understanding of programming concepts
• Familiarity with Python programming

Audience

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

Natural Language Generation (NLG) is a subfield of AI that focuses on generating human-like text. This course introduces participants to the fundamental concepts, techniques, and applications of NLG, helping them understand how AI can be used to automatically generate coherent and contextually relevant text.

This instructor-led, live training (online or onsite) is aimed at beginner-level professionals who wish to learn the basics of NLG and its role in AI and content generation.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of Natural Language Generation.
• Explore the applications of NLG in various industries.
• Learn basic techniques for generating human-like text using AI.
• Work with Python libraries and models to generate text.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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