AI use cases and applications
Machine Learning Techniques and Use Cases
Pick the technique that matches the data you have and the answer you need.
Supervised learning
Trained on labeled data; the labels act as the "supervisor".
- Classification predicts a category. Use cases: fraud detection, image classification, customer retention (churn), diagnostics.
- Regression predicts a continuous number. Use cases: house prices, sales forecasts, demand estimates.
Unsupervised learning
Trained on unlabeled data; the model finds structure on its own.
- Clustering groups similar data points. Use cases: customer segmentation, targeted marketing, recommendation systems.
- Dimensionality reduction cuts the number of features while keeping the important information, for example before visualization or to speed up training.
Reinforcement learning
An agent learns by trial and error in an environment, guided by rewards and penalties. Use it when you know what a good outcome is but not the path to get there.
Example: in AWS DeepRacer, the agent is the car, the environment is the track, the actions are throttle and steering, and the reward encourages finishing fast without leaving the track.
Quick decision guide
| You want to... | Technique |
|---|---|
| Predict a label from labeled examples | Classification |
| Predict a number from labeled examples | Regression |
| Find groups in unlabeled data | Clustering |
| Simplify data with many features | Dimensionality reduction |
| Learn a sequence of actions from rewards | Reinforcement learning |