Original subtitles

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.