Part 2/8:
Before diving into the technical steps, David emphasizes the importance of sourcing quality datasets. For those interested in machine learning, natural language processing, or large language models, he recommends several repositories:
Kaggle.com Datasets: A curated platform with diverse datasets, searchable by topic or type.
GitHub: A treasure trove of open-source datasets—simply search for specific datasets like "case law dataset."
Google Dataset Search: An excellent tool for discovering various datasets, often linking back to Kaggle or GitHub.
Gutenberg: Open-source texts suitable for NLP projects.
He suggests exploring these sources as they contain valuable, ready-to-use data ideal for training or testing models.