Unsupervised learning

We don’t always know what we’re looking for in data. That’s when unsupervised learning comes in. Instead of labels telling her what’s right or wrong, AI (artificial intelligence) gets raw data and tries to make sense of it. She looks for structure on her own.

Clustering

Think of clustering as sorting laundry. She groups socks with socks and shirts with shirts, even if no one told her what “sock” means. The rule of thumb: items that look alike end up together. We can use clustering for things like customer segments in marketing or finding similar songs in a playlist. It works well enough that we keep using it.

Dimensionality reduction

Data has a habit of getting too wide—hundreds of columns, each with numbers that may or may not matter. Dimensionality reduction trims it down so we can see the patterns. She finds smaller sets of features that still explain most of the data. One common trick is principal component analysis (PCA). Another is t-SNE. Don’t worry about the names; just know they make messy data easier to picture.

Why it matters

Unsupervised learning is less about right answers and more about hidden ones. She helps us notice things we didn’t think to look for. It’s not magic, but it’s a good way to stop drowning in raw numbers.

Our takeaway

We hand her the data, she quietly makes groups and maps, and we get a head start on understanding. As coders, we don’t have to overthink it. Use clustering when you want to see natural groups. Use dimensionality reduction when the data feels too big to hold in your head. That’s usually enough.