Original subtitles

Welcome back.

It's time to do a fun exercise.

Even though we've barely scratched the surface and we just started the course we're going to build a

YouTube recommendation engine.

But our own.

OK.

So how can we do that.

Well I have here a great Web site a machine learning playground.

And what we have here is a blank box.

I want you to open it up.

I'll link to this resource and try this out yourself as well.

Now let's imagine that on the y axis here it represents the length of the video.

That is the length of the YouTube video now.

In here we have the length and across the x axis.

That is right here.

Let's say that this represents the likes on the video.

So from less likes to more likes from shorter length courses to longer length and we look at our users

data let's say we have a user Bob and Bob likes to watch videos.

And this area and he has clicked like on these types of videos and with the purple.

If I click on purple here he has clicked dislikes on all these videos.

OK let's think about this.

So he has disliked a lot of videos that have lower likes from others and videos that seem to be shorter

and length and he has liked a lot of videos that have really good likes but tend to be longer and length

so if I click train here and we can ignore all these little buttons and the parameters let's just click

train.

This is what a machine learning model does.

It tries to predict based on data.

So we've given it this information of what Bob likes and what Bob dislikes.

And we trained it to figure out the pattern so that when we now recommend a video to Bob we know which

ones we should recommend and which ones we shouldn't.

For example let's say a new video is uploaded to YouTube and this video well right off the bat gets

a lot of likes and it gets a lot of likes and it's super long.

So it's right here.

Should we recommend this video to Bob.

Yes or no.

Well yes right.

Because from past data we've learned that we should recommend any videos that fall into this orange

category.

But let's say there's some new data point.

Let's say Bob starts watching new videos and then we see that.

Oh yeah.

Bob also likes these videos.

This videos these videos.

What happens.

Well let's train our model again.

And this is the new model that we created.

So now are machine learning model is telling us Hey recommend any videos to Bob that fall in this orange

category.

You see it's a little bit more complicated now.

So with each data point we're able to learn about what Bob's preferences are and then train the model

to decide if we should recommend and add the video to Bob's YouTube feed or we should not recommend

it because they're probably not going to watch what we just witnessed here is us building our own recommendation

engine.

Now obviously this is a simplified version but at the end of the day this is exactly what we want to

do.

We give inputs to machines and the machine decides and draws a line to figure out what we should predict

for a future input.

That is a new video comes up should we recommend it to Bob or should we not.

Congratulation you just created your own YouTube recommendation engine kind of.

Now I want to play around with this play around with the parameters.

Let's say we add five here and we train we do decision tree and click train.

Now you don't need to know anything about these just to play around and see what happens and I'll see

you in the next video.