The Difference Between Machine Learning and Statistics

Capturing real-world phenomena is an exercise in dealing with uncertainty. To do so, statisticians must understand the underlying distribution of the population under study, as well as come up with parameters that will provide predictive power. The goal for a statistician is to predict an interaction between variables with some degree of certainty (we are never 100% certain about anything). Machine learners, on the other hand, want to build algorithms that predict, classify, and cluster with the most accuracy. They operate without uncertainty or assumptions, continuously learning in order to improve their accuracy score.