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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.
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