Explaining Autoencoders seems to be a rite of passage for ML bloggers. It's conceptually understandable but has broad applications and is a jumping off point into other architectures.

Educational content tends to have a bias towards concepts that are easier to explain rather than what is most useful. But as far as the latter goes, Autoencoders are a good place to start.