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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 (5)
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
Jenna - TCMT
Course - Machine Learning with Python – 2 Days
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
Farris Chua
Course - Data Analysis in Python using Pandas and Numpy
1:1 very intensive but learnt a lot.
