All language subtitles for 06_jupyter-notebooks.en

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.