Original subtitles

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.