Original subtitles

From the videos, you've seen supervised learning and

unsupervised learning and also examples of both.

For you to more deeply understand these concepts,

I'll like to invite you in this class to see,

learn and maybe later write codes

yourself to implement these concepts.

The most widely used tool by machine learning and

data science practitioners today is the Jupyter Notebook.

This is the default environments that a lot of us

use to code up and experiment and try things out.

In this class, right here in your web browser,

you build a user Jupyter Notebook environment

to test out some of these ideas for yourself as well.

This is not some made up simplified environment,

this is the exact same environments,

the exact same tool, the Jupyter Notebook

that developers are using in

many large countries right now.

One type of lab that you see throughout

this class are optional labs,

which are ones you can open and run one line at

a time with usually no need to write any code yourself.

Optional labs are designed to be very easy

and I can guarantee you will get full marks,

every single one of them because there are no marks.

All you need to do is open it up

and just run the code we've provided.

By reading through and running

the code in the optional labs,

you see how machine learning code runs.

You should complete them relatively

quickly just by running it

one line at a time from top to bottom.

Optional labs are completely optional

so you don't have to do

them at all if you don't want to,

but I hope you will take a look

because running through them

will give you a deeper feel,

give you a little bit more experience

with what machine learning algorithms,

what machine learning code actually looks like.

Starting next week, there'll also be

some practice labs which would give you

an opportunity to write some of that code

yourself but we'll get to that next week.

Don't worry about it for

now and I hope you just go through

the next optional lab and get

through the rest of the content for this week.

Let's take a look at an example of a notebook.

Here's what you see when you

go to the first optional lab.

Feel free to scroll up and down and browse and mouseover

the different menus and take

a look at the different options here.

You might notice that there are

two types of these blocks,

also called cells in

the notebook and there are two types of cells.

One is what's called a Markdown cell,

which means a bunch of tax.

Here you can actually edit the text

if you don't like the text that we wrote,

but this is text that describes the code.

Then there's a second type of block

or cell which looks like this,

which has a code cell.

Here, we've already provided the code

and if you want to run this code cell,

hitting Shift Enter will run

the code in this code cell, and by the way,

if you click on a markdown cell,

so this showing all this formatting,

go ahead and hit Shift Enter on your keyboard as

well and that will also convert

back to this nicely formatted text.

This optional lab shows some common Python code,

so you can go ahead and run this

afterwards in your own Jupyter notebook.

When you jump into this notebook yourself,

what I'd like you to do is select

the cells and hit Shift Enter.

Read through the code, see if it makes sense,

try to make a prediction about what you

think this code would do and then

hit Shift Enter and

then see what the code actually does,

and if you like it,

feel free to go in and edit the code,

change the code, and then run it and see what happens.

If you haven't played in

the Jupyter Notebook environment for,

I hope you become more familiar with

Python in a Jupyter Notebook.

I spend a lot of hours playing around in

Jupyter notebooks and so I

hope you have fun with them too.

After that, I look forward to seeing you

in the next video where we'll take

the supervised learning problem as start to flesh

out our first supervised learning algorithm.

I hope that will be fun to you and

look forward to seeing you there.