Machine learning is an ever more rapidly advancing area of research, which, happily, is giving rise to interesting new schemes and varieties that algebraic geometers can sink their methodical teeth into. This talk will be an introduction to {\em algebraic statistics}, the bridge between these seemingly worlds-apart disciplines, and a host of many accessible but open algebraic geometry problems for 21st century mathematicians to solve