[MUSIC] Wow, exciting. We got through our first module. We got through a representation for linear classifier, in much more depth
than we did in the first course. In fact, we had a chance to dive in and really understand how these models are
built, the parameters and then the length foundation associated with them, as well
as how to extend it to other settings. So now we're able to take
our learning loop that goes from training data to creating features
h(x) to this logistic regression model. And define a learning algorithm which
we've talked in high level only now but we're going to talk in more detail in
the next module, to come up with w hat and we're going to make predictions from it. So, now you can take everything you've
learned and be able to talk about what does a linear classifier look like,
what's the logistic reaction model, how that relates to the general score
function, like we have with regression, but squeezes it to the interval 0,
1 to predict probabilities. How it's affected by parameters, and how
to extend that to multi class setting and how you do with category called data. So, we done a lot. We're really ready to really
think deeply about classifiers. [MUSIC]