In practical terms, deep learning is just a subset of machine learning. It technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged), but its capabilities are different.
Basic machine learning models do become progressively better at whatever their function is, but they still some guidance. If an ML algorithm returns an inaccurate prediction, then an engineer needs to step in and make adjustments. But with a deep learning model, the algorithms can determine on their own if a prediction is accurate or not.
Let’s go back to the flashlight example: it could be programmed to turn on when it recognizes the audible cue of someone saying the word “dark”. Eventually, it could pick up any phrase containing that word. Now if the flashlight had a deep learning model, it could maybe figure out that it should turn on with the cues “I can’t see” or “the light switch won’t work”. A deep learning model is able to learn through its own method of computing – its own “brain”, if you will.