Supervised Learning
Explore the fundamentals of supervised learning by understanding how algorithms use labeled examples to detect patterns and make predictions. Learn the two main phases—training and prediction—and see how this approach differs from traditional programming. This lesson also distinguishes supervised learning from unsupervised learning, providing a clear foundation for building machine learning models.
We'll cover the following...
We'll cover the following...
Introduction to supervised learning
Among the various approaches to ML, supervised learning is the one that reaped the most impressive results so far. Here is how supervised learning solves a problem like diagnosing pneumonia.
To do supervised learning, we’ll start from a set of examples, each carrying a label that the computer can learn from. For instance:
| What are we building? | Example | Label |
|---|---|---|
| A system that identifies a dog’s breed from its barking | A .wav recording of a dog |
The dog’s breed, like “greyhound” or “beagle” |
| A system that detects pneumonia | An X-ray scan | A boolean flag: 1 if the scan shows pneumonia, 0 if it doesn’t |
| A system that predicts the earnings of a lemonade stand from the weather | The recorded temperatures on a day in the past | The recorded earnings on |