The essence of it is this:

You have a model of some thing out there in the world. Ideally the model should be able to copy the behavior of that thing. That means it needs to produce the same data as that real thing.

So, you change parts of the model (numbers called parameters) until the model can create the data already collected from the real world system. This parameter- changing process is called training.

So for example, your model can be y=mx+b, a straight line, and the process of making sure m and b are good values to align the line to dataset (X, Y) is "training". AI models are not straight lines like y=mx+b, but the idea is the same. It's really advanced curve fitting, and some really interesting properties can emerge in the models as a result.