Now the word model can mean many different things depending on how it is used. So there are models when it comes to web frameworks those usually are different elements related to how you manage the data.

Model when it comes to data science and machine learning is the artifact created by the training process.

Now if that definition is a little bit vague I think looking at an example will make it easier to understand. So the way that you're typically going to use a model is it's going to relate to the weights, to the probabilities, to the regression data.

So as you're going through and you're building machine learning algorithms and you're implementing them every time that you are training data what you're doing is you're building the model. So if you're using a tool such as logistic regression right here each one of these data points when combined they create a data model.

When you're using a decision tree and the system goes in it builds that full set of weights and all of those conditions and it generates the tree that tree represents the model for your program.