Machine Learning is not just one technique – it’s a collection of learning methods. In this lesson, we’ll understand the **three core types of ML** and how they are applied in real life.
You train the model on **labeled data** — where both input and output are known. Examples: – Predicting house prices – Email spam detection – Classifying handwritten digits
The model works with **unlabeled data** — it finds patterns and groupings by itself. Examples: – Customer segmentation – Market basket analysis – Document clustering
A model **learns by trial and error** using rewards and penalties — like training a dog. Examples: – Self-driving cars – Game-playing AIs (like AlphaGo) – Robotics
| Feature | Supervised | Unsupervised | Reinforcement |
|---|---|---|---|
| Data Type | Labeled | Unlabeled | Environment + Rewards |
| Goal | Predict output | Find hidden structure | Maximize reward |
| Examples | Spam filter, price prediction | Customer clustering | Game playing, robotics |
👉 In the next lesson, you’ll explore the difference between AI, ML, and Deep Learning — the 3 most confusing tech terms!