But the point is that from some notion of error between our predicted values and

our observed values, we're gonna have some machine learning algorithm.

Again, we're leaving this for

the actual module on matrix factorization to describe what this algorithm is.

But what it's going to do is it's going to iteratively update our features for

the user and for the product until we get good agreement between our predicted

ratings and our actual observed ratings.

Okay, so in this module you learned how to do collaborative filtering in practice, so

now you can go out actually implement a recommender system.

You can do a gift recommender for your family,

which will make holiday shopping really easy.