Defining UL
UL is a type of machine learning (ML) that finds patterns in data without any prior training. Distinct from its counterpart, SL, where the model is trained using labeled data, UL algorithms work with unlabeled data. The aim is to model the underlying structure or distribution in the data to learn more about it.
Think of it as a detective who walks into a crime scene with no initial clues or suspects. The detective’s job is to uncover patterns, find hidden groups, or establish relationships between different elements at the scene.
Practical examples of UL
To make this concept more tangible, let’s look at some practical examples:
- Market research: A company wants to understand its customer base better and tailor their marketing to different consumer segments. They have a wealth of data (for example, customer data or consumer survey data) but no specific categories or labels. UL can help identify distinct groups or segments within their customers...
