Machine Learning Simplified

As we discussed previously, Machine Learning refers to algorithms that are used to identify patterns within data. But what exactly do we mean by "patterns", what all can we do with ML, and what is all this jargon about "models" and "training" them. In this article, I'll try to explain all this without getting too technical, and what you, as a business-user, should know about Machine Learning. Supervised Learning implies use-cases where we have a target we're trying to predict given the data. Supervised algorithms enable us to predict the target (for example the estimated credit limit, tractor sales, if the customer will churn, or the mail category) using the input data (customer's credit history, weather and macroeconomic conditions, customer's activity on the platform, mail specifications). There are models both for Regression and Classification problems, i.e. algorithms which can solve these types of problems.