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Course Outline
Introduction to Natural Language Generation (NLG)
- Overview of NLG and its applications
- Understanding the NLG pipeline
- Introduction to Python libraries for NLG
Data Collection and Preparation
- Collecting data from various sources
- Cleaning and preprocessing text data
- Organizing content for generation
Language Modeling for NLG
- Introduction to language models
- Training a language model for text generation
- Fine-tuning language models using SpaCy and NLTK
Sentence Planning and Text Structuring
- Planning sentence structure and content flow
- Using templates for text generation
- Customizing text structure based on use cases
Content Generation and Post-Processing
- Generating text from structured data
- Evaluating and refining generated content
- Post-processing and formatting output
Advanced NLG Techniques
- Using neural networks for text generation (e.g., GPT models)
- Handling context and coherence in generated text
- Exploring real-world applications and case studies
Final Project: Building an NLG System
- Defining a project scope
- Building and deploying an NLG system
- Testing and evaluating the system
Summary and Next Steps
Requirements
- Python programming experience
Audience
- Developers
- Data scientists
21 Hours
Testimonials (2)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
Examples/exercices perfectly adapted to our domain
