You've heard about it, but do you understand? Everything you need to know about machine learning

Looking through this lens, ML seems to be a lot like statistical modelling. In statistical modelling, we collect data, verify that it is clean -- in other words, that we have completed, corrected, or deleted any incomplete, incorrect, or irrelevant parts of the data -- and then use this clean dataset to test hypotheses and make predictions and forecasts. The idea behind statistical modelling is the attempt to represent complex issues in relatively generalizable terms, which is to say, terms that explain most events studied. Effectively, we programme the algorithm to perform certain functions based on the data we submit. Put differently, the algorithm is static.