When we classify Machine Learning on the basis of how our system generalizes or predicts we end up with two types :
Let's see what these two things mean :
A system is called to be learning by instance when it learns by heart from the data provided to it and thus generalizes or predicts on the basis of some similarity measure or similar feature that it found occurring on every data or some data .
I have explained both the types here with an example fir better understanding :
A system is called model based when it learns from the data and creates a model, which has some parameters and it predicts the output by using this data trained model.
I would not get into the mathematics but for better understanding you can imagine a model as a equation and the parameter (theta) and the input data (x) as variables in it .
And by using optimizing techniques like Gradient Descent we find an optimal value of theta .
Thus when we substitute both the optimized parameter and the input value i.e the test data into the model or the equation and we get the best output or prediction .