All language subtitles for 04_unsupervised-learning-part-1.en

After supervised learning,

the most widely used form of machine
learning is unsupervised learning.

Let's take a look at what that means,
we've talked about supervised learning and

this video is about unsupervised learning.

But don't let the name uncivilized for
you,

unsupervised learning is I think just
as super as supervised learning.

When we're looking at supervised
learning in the last video recalled,

it looks something like this in
the case of a classification problem.

Each example, was associated with
an output label y such as benign or

malignant, designated by the poles and
crosses in unsupervised learning.

Were given data that isn't
associated with any output labels y,

say you're given data on patients and
their tumor size and the patient's age.

But not whether the tumor was benign or
malignant, so

the dataset looks like this on the right.

We're not asked to diagnose
whether the tumor is benign or

malignant, because we're
not given any labels.

Why in the dataset, instead,
our job is to find some structure or

some pattern or just find
something interesting in the data.

This is unsupervised learning,

we call it unsupervised because we're
not trying to supervise the algorithm.

To give some quote right answer for
every input, instead,

we asked the our room to figure out
all by yourself what's interesting.

Or what patterns or
structures that might be in this data,

with this particular data set.

An unsupervised learning algorithm,
might decide that

the data can be assigned to two different
groups or two different clusters.

And so it might decide, that there's
one cluster what group over here,

and there's another cluster or
group over here.

This is a particular type of unsupervised
learning, called a clustering algorithm.

Because it places the unlabeled data,
into different clusters and

this turns out to be used
in many applications.

For example,
clustering is used in google news,

what google news does
is every day it goes.

And looks at hundreds of thousands of
news articles on the internet, and

groups related stories together.

For example, here is a sample from
Google News, where the headline of the top

article, is giant panda gives birth to
rear twin cubs at Japan's oldest zoo.

This article has actually caught my eye,
because my daughter loves pandas and so

there are a lot of stuff panda toys.

And watching panda videos in my house,
and looking at this,

you might notice that below this
are other related articles.

Maybe from the headlines alone,

you can start to guess what
clustering might be doing.

Notice that the word
panda appears here here,

here, here and here and
notice that the word

twin also appears in all five articles.

And the word Zoo also appears
in all of these articles, so

the clustering algorithm
is finding articles.

All of all the hundreds of thousands of
news articles on the internet that day,

finding the articles that mention similar
words and grouping them into clusters.

Now, what's cool is that this clustering
algorithm figures out on his own which

words suggest, that certain
articles are in the same group.

What I mean is there isn't an employee at
google news who's telling the algorithm to

find articles that the word panda.

And twins and
zoo to put them into the same cluster,

the news topics change every day.

And there are so many news stories,
it just isn't feasible to people

doing this every single day for
all the topics that use covers.

Instead the algorithm has to figure
out on his own without supervision,

what are the clusters
of news articles today.

So that's why this clustering algorithm,

is a type of unsupervised
learning algorithm.

Let's look at the second example
of unsupervised learning

applied to clustering genetic or DNA data.

This image shows a picture
of DNA micro array data,

these look like tiny
grids of a spreadsheet.

And each tiny column represents
the genetic or DNA activity of one person,

So for example, this entire Column
here is from one person's DNA.

And this other column
is of another person,

each row represents a particular gene.

So just as an example, perhaps this
role here might represent a gene that

affects eye color, or this role here is
a gene that affects how tall someone is.

Researchers have even found a genetic
link to whether someone dislikes certain

vegetables, such as broccoli, or
brussels sprouts, or asparagus.

So next time someone asks you why
didn't you finish your salad,

you can tell them,
maybe it's genetic for DNA micro race.

The idea is to measure how much
certain genes, are expressed for

each individual person.

So these colors red, green, gray,
and so on, show the degree to

which different individuals do, or
do not have a specific gene active.

And what you can do is then run
a clustering algorithm to group

individuals into different categories.

Or different types of people like maybe
these individuals that group together,

and let's just call this type one.

And these people
are grouped into type two,

and these people are groups as type three.

This is unsupervised learning,
because we're not telling the algorithm in

advance, that there is a type one
person with certain characteristics.

Or a type two person with
certain characteristics,

instead what we're saying
is here's a bunch of data.

I don't know what the different
types of people are but

can you automatically
find structure into data.

And automatically figure out whether
the major types of individuals,

since we're not giving the algorithm the
right answer for the examples in advance.

This is unsupervised learning,
here's the third example,

many companies have huge databases of
customer information given this data.

Can you automatically
group your customers,

into different market segments so that you
can more efficiently serve your customers.

Concretely the deep learning dot AI team
did some research to better understand

the deep learning dot AI community.

And why different individuals
take these classes,

subscribed to the batch weekly newsletter,
or attend our AI events.

Let's visualize the deep
learning dot AI community,

as this collection of
people running clustering.

That is market segmentation found
a few distinct groups of individuals,

one group's primary motivation is
seeking knowledge to grow their skills.

Perhaps this is you, and so that's great,

a second group's primary motivation is
looking for a way to develop their career.

Maybe you want to get a promotion or
a new job, or

make some career progression if this
describes you, that's great too.

And yet another group wants to stay
updated on how AI impacts their

field of work, perhaps this is you,
that's great too.

This is a clustering that our team used
to try to better serve our community

as we're trying to figure out.

Whether the major categories of learners
in the deeper and community, So

if any of these is your top motivation for
learning, that's great.

And I hope I'll be able to help you on
your journey, or in case this is you, and

you want something totally different
than the other three categories.

That's fine too, and I want you to know,
I love you all the same, so

to summarize a clustering algorithm.

Which is a type of unsupervised
learning algorithm,

takes data without labels and tries to
automatically group them into clusters.

And so maybe the next time you see or
think of a panda,

maybe you think of clustering as well.

And besides clustering, there are other
types of unsupervised learning as well.

Let's go on to the next video,

to take a look at some other types
of unsupervised learning algorithms.