# Supervised, Unsupervised and Reinforcement Learning — Machine Learning Basics

Source: https://www.skillbyai.com/en/machine-learning/i-types

> Three families of problems.

## Labels, structure, rewards

**Supervised learning** uses labelled examples: **regression** predicts a number (house price, delivery time) and **classification** predicts a category (spam or not, which flower). **Unsupervised learning** has no labels and finds structure: **clustering** groups similar items, **dimensionality reduction** compresses features, **anomaly detection** finds unusual items. **Reinforcement learning** learns actions by trial and error from rewards (games, robotics, some recommendation and control systems). Most business ML is supervised, so most of this course focuses there.

## Problem types with examples

Match the question to the family.

```text
question                                        family           output
how much will this house sell for?             regression        a number
is this transaction fraudulent?                classification    a class (yes/no)
which customers behave alike?                  clustering        group ids
which sensor readings are unusual?             anomaly detection a flag / score
how should a robot move to reach the goal?     reinforcement     actions
```

## Phrase the question precisely

Write the exact prediction you need, its unit and when it must be available; vague goals lead to the wrong problem type.

**Quiz:** Predicting tomorrow's sales in rupees is which kind of problem?

- [x] Regression
- [ ] Classification
- [ ] Clustering
- [ ] Reinforcement learning

*Answer:* Regression. A numeric target means regression.