What is Gaussian Mixture Models (GMMs)?

Gaussian Mixture Models (GMMs) are like having different types of candies in a bag, each type has its own flavor and shape.

Imagine you have a big jar full of jelly beans, gummy bears, and chocolate chips. You can't see inside the jar, but you can take out handfuls and guess what’s inside based on how they look and feel. That's kind of like what GMMs do, they help us figure out how many different kinds of things are mixed together in a group.

GMMs use something called normal distributions, think of them as invisible rules that describe how the candies (or data points) look on average. Each type of candy follows its own rule, and GMMs try to find out which rule applies to which candy.

It's like having a group of friends who all have different ways of drawing, some draw fast, others take their time. GMMs help you figure out how many friends there are and what each one’s style is like, just by looking at the drawings.

GMMs are useful when we want to find hidden groups in data, like figuring out different customer types from shopping habits or identifying clusters of similar animals in a forest. They're like detective work, helping us see patterns that aren’t obvious at first glance.