Preparing Text for AI Models

Overview

The Preparing Text for AI Models course is designed for developers, engineers, and technical product builders who are new to Generative AI but already possess intermediate machine learning knowledge, basic Python proficiency, and familiarity with development environments such as VS Code, and who want to engineer, customize, and deploy open generative AI solutions while avoiding vendor lock-in. The course equips learners with practical skills in dataset sourcing, preprocessing, and formatting for training large language models. Starting with the discovery of text datasets from repositories like Hugging Face, Kaggle, and Common Crawl, learners evaluate quality, relevance, and licensing considerations. The course then covers preprocessing pipelines, including text cleaning, normalization, deduplication, and tokenization strategies, ensuring efficiency and compatibility with model training. Learners also design annotation schemas, apply semi-automated labeling techniques, and build validation workflows to maintain quality. The final module guides learners in constructing structured datasets for instruction tuning, fine-tuning, and benchmarking, supported by best practices in train-test splits and stratification. By the end of the course, learners will have created production-ready text datasets suitable for generative AI applications.

Syllabus

Dataset Discovery and Acquisition for Text

In this module, you’ll be introduced to key resources you can add to your toolkit for sourcing text datasets. You’ll navigate repositories like Hugging Face, Kaggle, and Common Crawl, and learn how to evaluate dataset size, quality, and relevance to your training goals. You’ll also cover legal and ethical considerations and practice importing and converting datasets between common formats, so you can confidently select and prepare text data for your projects.

Text Data Processing and Formatting

In this module, you’ll apply text-cleaning techniques, compare different tokenization methods, and design preprocessing pipelines. You’ll also format data for instruction tuning and build batching routines, giving you hands-on experience with multiple approaches you can adapt to your own training workflows.

Advanced Annotation and Tagging

In this module, you’ll learn how to turn raw text into structured datasets that are ready for training. You’ll design and apply annotation schemas, practice splitting datasets for training and evaluation, and compare approaches for organizing data. Along the way, you’ll see how different methods affect model performance, giving you the judgment to decide which structuring strategies work best for your projects.