Machine learning basically involves feeding computers large amounts of data and letting them analyse that data to extract patterns from which they can draw conclusions. You have probably seen this in action with face recognition technology (such as on Facebook or modern digital cameras and smartphones), where the computer can identify and frame human faces in photographs.
In order to do this, the computers are referencing an enormous library of photos of people’s faces and have learned to spot the characteristics of a human face from shapes and colours averaged out over a dataset of hundreds of millions of different examples. This process is basically the same for any application of machine learning, from fraud detection (analysing purchasing patterns from credit card purchase histories) to generative art (analysing patterns in paintings and randomly generating pictures using those learned patterns).