All language subtitles for 05_unsupervised-learning-part-2.en

In the last video, you saw

what is unsupervised learning,

and one type of unsupervised
learning called clustering.

Let's give a slightly
more formal definition

of unsupervised learning

and take a quick look at

some other types of

unsupervised learning
other than clustering.

Whereas in supervised learning,

the data comes with
both inputs x and

input labels y, in
unsupervised learning,

the data comes only with inputs

x but not output labels y,

and the algorithm has to find

some structure or some pattern

or something interesting
in the data.

We're seeing just one example of

unsupervised learning called
a clustering algorithm,

which groups similar
data points together.

In this specialization,
you'll learn about

clustering as well as

two other types of
unsupervised learning.

One is called anomaly detection,

which is used to
detect unusual events.

This turns out to be
really important for

fraud detection in
the financial system,

where unusual events,
unusual transactions could

be signs of fraud and for
many other applications.

You also learn about
dimensionality reduction.

This lets you take

a big data-set and almost
magically compress it

to a much smaller data-set while

losing as little
information as possible.

In case anomaly detection and

dimensionality
reduction don't seem

to make too much
sense to you yet.

Don't worry about
it. We'll get to

this later in the
specialization.

Now, I'd like to ask you

another question to help you
check your understanding,

and no pressure, if
you don't get it

right on the first
try, is totally fine.

Please select any
of the following

that you think are examples
of unsupervised learning.

Two are unsupervised
examples and two

are supervised learning
examples. Please take a look.

Maybe you remember the
spam filtering problem.

If you have labeled data you now

label as spam or
non-spam e-mail,

you can treat this as a
supervised learning problem.

The second example, the
news story example.

That's exactly the
Google News and

tangible example that you
saw in the last video.

You can approach that using

a clustering algorithm to
group news articles together.

That we'll use
unsupervised learning.

The market segmentation example

that I talked about a
little bit earlier.

You can do that as

an unsupervised learning
problem as well because you can

give your algorithm
some data and ask it

to discover market
segments automatically.

The final example on
diagnosing diabetes.

Well, actually that's a lot like

our breast cancer example

from the supervised
learning videos.

Only instead of benign
or malignant tumors,

we instead have diabetes
or not diabetes.

You can approach this as a
supervised learning problem,

just like we did for the

breast tumor
classification problem.

Even though in the last video,

we've talked mainly about
clustering, in later videos,

in this specialization, we'll
dive much more deeply into

anomaly detection and
dimensionality reduction as well.

That's unsupervised learning.

Before we wrap up this section,

I want to share
with you something

that I find really exciting,

and useful, which is the use of

Jupyter Notebooks in
machine learning.

Let's take a look at
that in the next video.