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

Delivery Options

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