AI is only as good as the data behind it, which means understanding data is one of the most practical skills you can build. These guides cover the full data lifecycle for AI projects, from collecting and cleaning datasets to labelling, storing, and versioning them for training. You will learn about data quality issues that silently ruin AI outputs, privacy regulations you need to follow when handling personal information, and techniques like data augmentation that help you do more with less. The topic also explores synthetic data, bias in training sets, and how to evaluate whether your data is good enough for a given task. Whether you are preparing data for a machine learning project, auditing an AI vendor's data practices, or setting data governance policies for your organisation, these guides give you the practical knowledge to work confidently with the data that powers AI systems.
Learn to prepare data for AI and machine learning. From cleaning to transformation—practical guidance for the often-overlooked work that makes AI possible.
Generate training data with AI: create examples, augment datasets, and bootstrap models when real data is scarce or sensitive.