Intro to Unsupervised Learning
Learning without an answer key
In supervised learning every example comes with the right answer. In unsupervised learning there are no labels at all — just raw data, and the goal is to discover the structure hiding inside it.
That makes it both powerful and tricky: powerful because unlabelled data is cheap and abundant, tricky because there's no answer key to score against. Success is judged by whether the structure it finds is useful.
Supervised: "here are spam emails labelled spam — learn to predict the label." Unsupervised: "here are a million emails — group the similar ones," with nobody saying what the groups should be.
The three core tasks
From one unlabelled cloud of points, watch clustering, dimensionality reduction, and anomaly detection each extract something different.
What each task does
Partition data into groups of similar points. See K-Means and Hierarchical Clustering.
Squeeze many correlated features into a few, keeping the signal. See PCA.
Flag points that don't fit any pattern — fraud, faults, intrusions.
Where it shows up
Group customers by behaviour to target each segment differently.
Cluster products or users to suggest "more like this".
Reduce high-dimensional data to 2D so you can see and explore it.
Modern LLMs learn from unlabelled text by inventing their own prediction task.
The honest challenge
- Works on cheap, unlabelled data
- Reveals patterns nobody thought to look for
- Great for exploration before modelling
- No ground truth to validate against
- Results can be hard to interpret
- Often needs you to choose K or a threshold