[MUSIC] This is going to be a highly hands on
course, a little tough, hard work, but you're going to come out
really an expert on the topics. As usual we're going to keep on our core
philosophy, which is to always use case studies to motivate everything that we do
and you'll see that throughout the course. But there's also six things I
think about in every module and you'll see that happening. We'll talk about core concepts. Core concepts that go beyond
the techniques in that particular module. I've created visualizations for
every aspect of the course. You'll see new kinds of
visualizations that we hope will help you really grasp
the underlying ideas. We'll talk about actual
algorithms in detail that make it possible to build
these things in practice. So we'll really cover the algorithms but we won't cover everything
about classification. We'll narrow down the course to
cover techniques that are really impacting the world today,
that are actually practical. And we'll talk about what it takes to
make those techniques practical, and that's a big thing,
because you're going to implement them, you're going to implement them
from scratch in this course. All the algorithms that we're going to
talk about, you'll implement from scratch, and you'll be able to take them into the
real world, and implement them yourselves, if you're interested in them or
use them from a package. But, really understand where they're
coming from. For every module we cover in this course, we've created some additional
advanced topics in advanced areas. We're going to mark those as optional. For those interested, you'll be
able to dig in, for every module, new things you might want to learn
if you want to go into more depth. But those are optional
You don't need them. Even if you skip all of those you
still have a really fantastic handle on some of the most important techniques
that actually work in the real world to build classifiers. We're not going to cover everything but
we're going to cover in depth now for you to be able to really understand that. So we're going to focus on models
that make the most difference. So that's linear classifiers,
logistic regression, decision trees, and ensemble methods. And if you can understand those, you'll be
able to create the kinds of classifiers that you need to get amazing accuracy
on a wide range of real world problems. We're just going to talk
about fundamental algorithms, and you're going to implement them and
be very proficient at them. Gradient, which we also talked
about in the regression course. Stochastic gradient which is useful for scaling gradient methods and
classifiers to really massive problems. Recursive greedy algorithms that
are useful to learn decision trees. And Boosting, which is an amazing technique,
which is useful for running ensembles of classifiers. And we will discuss core machine learning
concepts which are useful way beyond the content of this course. Way beyond classifiers, so things like how to avoid overfitting,
how to deal with missing data. Precision-recall and online learning. It's going to be action-packed. [MUSIC]