Let's talk about data. What does this word mean? Well, it's used in all kinds of contexts today, but I think in a technical or practitioner context we mean specifically information which can be used by computers. Now in the context of machine learning, we are interested in understanding data as composed of columns, or what might be called variables. So, data here is always tabular in our model, in our thinking about the problem. So we have some variables that say X1, X2, X3, and we have a Y, and we are thinking in terms of a tabular layout, and what we mean by a variable then is such a column, or variable, and here we use the letter X to denote a feature or what you might call an observation, an example, a trait or characteristic, something else. So here Y we call the target and that is the thing that we are trying to predict. So prediction target. Now there are different kinds of connection that Y may have to X. In general, we call such connections functions or relationships and the machine using statistics is able to determine from a historical data set, and then by using such a formula able to, in the future, estimate or predict something for Y given what it can see for X.