[MUSIC]
Okay, so now that you guys are psyched about the future machine running,
let's talk about what we're gonna cover in the specialization.
Okay, so in the regression course, now that you guys know what regression is,
we're gonna go through a lot more of the details on different formulations
for models.
For example, how to cope with lots of features,
something that we eluded to in this course.
And we're also gonna talk about, in great detail, algorithms for
fitting these models.
So different optimization algorithms, remembering now that we've seen that
there's this cost we can talk about, residual sum of squares and minimizing it.
We're gonna talk about different optimization algorithms like gradient
descent and coordinate descent for actually doing this optimization.
And then, through this case study in predicting house prices,
we're gonna cover a lot of concepts that are really foundational to machine
learning in many different areas.
And some of these include how do we think about measuring cost.
How do we think about choosing between models and
dealing with overfitting of our model.
So we're gonna explore this in this context, but again, these ideas generalize
well beyond regression and well beyond predicting house prices.
Then when we get to the classification course,
we're gonna talk about specific examples of linear classifiers.
We're also gonna talk about methods of how to scale up to using lots and
lots of features and
creating classifiers in this very high dimensional feature representation.
And again, we're gonna talk about algorithms for
performing these types of classifications.
Specifically, looking at an optimization algorithm that allows us to scale up to
really, really large data sets.
And we're also gonna talk about this idea of how
we can think about blending different models using something called boosting.
And again, we'll look at many different concepts.
And one that I think is really interesting is how to do something called
online learning where we get data that just continually streams in and
we like to make our inferences continually as we get that data.
Then when we get to clustering and retrieval, again,
we've gone through the foundational ideas of what does it mean to do clustering and
what is our document retrieval task.
But we're gonna step it up even more where now, for example,
when we think about clustering, a document might not just be about sports or
world news or science or entertainment.
Maybe, a document has some mixture of different topics.
We can very easily think about a document that's about both finance and world news.
And so we're gonna think about how we model
this more complicated structure that might be present in our data.
And for the algorithmic side of things, we're gonna look at very efficient
ways for searching over data when we're doing our retrieval tasks.
And lots of different algorithms for
doing the types of clustering models that we're talking about.
And in terms of really important concepts that we're gonna cover in this course,
one thing is thinking about how to scale up doing the clustering to a really,
really massive collections of documents using something called map-reduce.
Next, we're gonna turn to our Recommender Systems and
Dimensionality Reduction course.
And here, beyond the types of collaborative filtering and
matrix factorization that we already talked about in this course.
We're also gonna talk about ways to take high dimensional data sets and
think about modelling in terms of some lower dimensional representation.
And so for that, we're gonna think about some algorithms for
doing this dimensionality reduction.
And we're also gonna talk about algorithms for
fitting the types of matrix factorization models that we described in this course.
And in this case, some of the important concepts we're gonna go through
especially when we're thinking about matrix factorization is how we think about
doing something like matrix completion.
And that's where we're filling in all the unknown squares,
if you remember that from this course.
And then a more general problem which is this cold-start problem where we,
in the case of our recommender systems, might have no information about a user or
a product and want to form those recommendations.
And finally, we're gonna get to the Capstone which is really,
as I hope you guys have got in a sense, gonna be very, very cool.
And now that you've gone through this course, you understand some of
the concepts that we talked about in terms of what makes up this Capstone.
In particular, we're gonna look at a recommender system
that combines ideas of doing text sentiment analysis with important
ideas from computer vision in terms of searching over different images.
And for doing this, we're gonna use deep learning, so
there's gonna be some really important and
more detailed information about deep learning presented in the Capstone.
So please get to that point.
It's really gonna be cool and very important and all of this
is gonna allow you to build this really intelligent web application and
deploy it and do things that impress,
not just your friends and family, but also potential employers.
So of course that's a potential bonus to some of you out there.
But it's really gonna be a lot of fun.
So we hope you get to that point and enjoy the Capstone project.
[MUSIC]