Machine learning solves problems in three ways:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Let’s explore each one!
Supervised learning is about providing AI with enough examples to make accurate predictions.
All supervised learning algorithms need labeled data. Labeled data is data that is grouped into samples that are tagged with one or more labels. In other words, applying supervised learning requires you to tell your model:
- What the key characteristics of a thing are, also called features
- What the thing actually is
For example, the information might be drawings and photos of animals, some of which are dogs and are labeled “dog”. The machine will learn by identifying a pattern for “dog”. When the machine sees a new dog photo and is asked, “What is this?”, it will respond, “dog”, with high accuracy. This is known as a classification problem.