What's the Difference Between Supervised, Unsupervised, and Reinforcement Learning?

In business, supervised machine learning is the most common type. You give the AI input data and the correct answers - that’s so it can learn to predict the right answer for new, similar data.

Think of it like studying for a test with answer sheets. You show the model thousands of examples where you already know the outcome. For loan applications, you'd show it past applications along with whether each loan was approved or denied. The model learns what factors lead to approval.

This approach works well for classification tasks (spam or not spam, approve or deny) and prediction tasks (how much will this customer spend, what's the likelihood of churn).

Finding Hidden Patterns: Unsupervised Learning

Unsupervised learning is like being a detective looking for clues without knowing what crime was committed. The AI looks at data without any labels or correct answers and tries to find meaningful patterns or groupings.

A common use is customer segmentation. You give the AI customer data - what they buy, when they shop, how much they spend. It can group them into segments based on what it’s found. Often, you’ll find groups like “bargain hunters,” “premium buyers,” or “weekend shoppers” despite it never being told to look for them specifically.

This is excellent for market research! Use it to identify anomalies or discover trends you didn't know were there.