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What are the different types of machine learning?

Answer: There are three main types of machine learning:

1. Supervised Learning:

It involves training a model on a labeled dataset, where the input data is paired with the correct output. The model learns to map inputs to outputs.

Imagine you’re teaching a computer like you would teach a pet. You show it examples of things and tell it what they are.

For example, you show it pictures of cats and dogs, and you tell it which is which. The computer learns to recognize cats and dogs based on these examples.

2. Unsupervised Learning:

In this type, the model is given only input data and is tasked with finding patterns or structures in the data without any explicit labels or targets.

For example, if you had a collection of different fruits but they weren’t labeled, the computer might try to group similar fruits together based on things like color, size, and shape. It might discover that apples and oranges are similar in certain ways, even if it doesn’t know what they’re called.

3. Reinforcement Learning:

This type involves an agent that learns to make a sequence of decisions to achieve a goal in an environment. It receives feedback in the form of rewards or penalties for its actions.

Think of this like training a dog. You want the dog to perform certain tricks, so you give it treats when it does something right and maybe a little scolding when it does something wrong. The dog learns to do the tricks to get more treats.