You might be more or less asking how a neural network works. That's what a lot of people will be thinking about when they think of the phrase machine learning.

A neural network is only a category of machine learning. It's not the whole picture.

Machine learning can be something complex like a neural network. But it can also be something very simple, like Menace. A simple to understand process, popularly known for learning tic-tac-toe.

Machine learning is all based on a goal, score, and reward/punishment system. It's a program that has a goal, gets somehow scored based on the results it gives, and receives a change relative to the difference.

The difference between the results and the goal is often called error. And the error is used to create the change. This change can be seen as a punishment or reward.

A* pathfinding isn't exactly machine learning. But I like to include this here too. Because it also uses some goal, score, and punishment/reward techniques. It can help get the right idea about how to compute to solve a problem.

A neural network is extremely difficult to wrap your head around. Particularly for obscure tasks like driving a car or creating images. They can be extraordinarily complex. It's a line formula (oftentimes multidimensional) that approximates a line you want given a set of point values as a goal. There's a lot of calculus and angry math involved.

Imagine trying to figure out a line formula that draws the path of a roller coaster. Then imagine a formula with variables that can be adjusted to draw the path of any roller coaster.

The Taylor Series is again, not machine learning. But can give you a little taste of what it's like behind the scenes of a neural network. Some of the math is kinda similar.