Machine learning, as people have pointed out, is broad. However, I think that understanding gradient descent in general really gets to the heart of most new applications (especially neural networks).

Gradient Descent is kind of like a game of hotter/colder. You start by walking in a completely random direction, and then someone tells you you're either getting warmer or getting colder.
A neural network starts similarly, taking it's input and doing a bunch of random multiplications and getting random output. Then you tell it what the answers should have been and it knows how far off it was. Then it goes back to all those random variables (parameters) and calculates how much each one contributed to it being wrong, and adjusts them ever so slightly so that they would have produced a better result.