All language subtitles for 01_what-is-machine-learning.en

What is machine learning?

In this video, you'll learn
the definition of what it is

and also get a sense of when
you might want to apply it.

Let's take a look together.

Here's a definition of what is

machine learning that is
attributed to Arthur Samuel.

He defined machine learning

as the field of study that gives

computers the ability to learn

without being
explicitly programmed.

Samuel's claim to fame was
that back in the 1950s,

he wrote a checkers
playing program.

The amazing thing about

this program was
that Arthur Samuel

himself wasn't a very
good checkers player.

What he did was he had
programmed the computer

to play maybe tens of thousands
of games against itself.

By watching what social
support positions

tend to lead to wins
and what positions

tend to lead to losses the
checkers plane program

learned over time what are

good or bad suport positions by

trying to get a good and
avoid bad positions,

this program learned to get
better and better at playing

checkers because
the computer had

the patience to play

tens of thousands of
games against itself.

It was able to get

so much checkers
playing experience that

eventually it became a
better checkers player

than also, Samuel himself.

Now throughout these videos,

besides me trying to
talk about stuff,

I occasionally ask
you a question

to help make sure you
understand the content.

Here's one about what happens if

the computer had played
far fewer games.

Please take a look and pick

whichever you think
is the better answer.

Thanks for looking at the quiz.

If you had selected

this answer would have

made it worse then
you got the right.

In general, the
more opportunities

you give a learning
algorithm to learn,

the better it will perform.

If you didn't select the
correct answer the first time,

that's totally okay too.

The point of these
questions isn't

to see if you can get them

all correctly on the first try.

These questions are
here just to help

you practice the concepts
you are learning.

Arthur Samuel's
definition was a rather

informal one but in
the next two videos,

we'll dive deeper
together into what are

the major types of machine
learning algorithms?

In this course, you learn about

many different
learning algorithms.

The two main types of
machine learning are

supervised learning and
unsupervised learning.

We'll define what
these terms mean

more in the next
couple of videos.

Of these two,
supervised learning

is the type of machine
learning that is used most in

many real-world
applications and has

seen the most rapid
advancements and innovation.

In this specialization,

which has three
courses in total,

the first and
second courses will

focus on supervised learning,

and the third will focus
on unsupervised learning,

recommender systems, and
reinforcement learning.

By far, the most used types of

learning algorithms today
are supervised learning,

unsupervised learning,
and recommender systems.

The other thing we're
going to spend a lot of

time on in this specialization

is practical advice for
applying learning algorithms.

This is something I feel
pretty strongly about.

Teaching about learning
algorithms is like giving

someone a set of tools
and equally important,

so even more important
to making sure you

have great tools is making sure

you know how to apply them

because like is it is
somewhere where it

gives you a state-of-the-art hammer

or a state-of-the-art hand drill
and say good luck.

Now you have all
the tools you need

to build a three-story house.

It doesn't really work like that

and so too, in machine learning,

making sure you
have the tools is

really important
and so is making

sure that you know how to apply

the tools of machine
learning effectively.

That's what you
get in this class,

the tools as well as the

skills to apply
them effectively.

I regularly visit
with friends and

teams in some of the
top tech companies,

and even today I see experienced
machine learning teams

apply machine learning
algorithms to some problems,

and sometimes they've
been going at it for

six months without much success.

When I look at what
they're doing,

I sometimes feel like
I could have told them

six months ago that the
current approach won't work

and there's a
different way of using

these tools that will give

them a much better
chance of success.

In this class, one of

the relatively unique
things you learn is

you learn a lot about
the best practices for

how to actually
develop a practical,

valuable machine
learning system.

This way, you're less likely to

end up in one of
those teams that

end up losing six months going
in the wrong direction.

In this class, you
gain a sense of how

the most skilled machine

learning engineers
build systems.

I hope you finish

this class as one of
those very rare people in

today's world that know how to

design and build serious
machine learning systems.

That's machine learning.

In the next video,

let's look more
deeply at what is

supervised learning and also

what is unsupervised learning.

In addition, you'll learn

when you might want
to use each of them,

supervised and
unsupervised learning.

I'll see you in the next video.