All language subtitles for 01_linear-regression-model-part-1.en

In this video,
we'll look at what

the overall process of
supervised learning is like.

Specifically, you see
the first model of

this course, Linear
Regression Model.

That just means fitting a
straight line to your data.

It's probably the most

widely used learning
algorithm in the world today.

As you get familiar
with linear regression,

many of the concepts you
see here will also apply to

other machine
learning models that

you'll see later in
this specialization.

Let's start with a
problem that you can

address using linear regression.

Say you want to
predict the price of

a house based on the
size of the house.

This is the example we've
seen earlier this week.

We're going to use a dataset on

house sizes and
prices from Portland,

a city in the United States.

Here we have a graph where

the horizontal axis is

the size of the house
in square feet,

and the vertical axis is

the price of a house in
thousands of dollars.

Let's go ahead and plot

the data points for various
houses in the dataset.

Here each data point,

each of these little
crosses is a house with

the size and the price that

it most recently was sold for.

Now, let's say you're
a real estate agent in

Portland and you're helping
a client to sell her house.

She is asking you, how

much do you think I can
get for this house?

This dataset might help you

estimate the price
she could get for it.

You start by measuring
the size of the house,

and it turns out that the
house is 1250 square feet.

How much do you think this
house could sell for?

One thing you could do this,

you can build a linear

regression model
from this dataset.

Your model will fit a
straight line to the data,

which might look like this.

Based on this straight
line fit to the data,

you can see that the house
is 1250 square feet,

it will intersect the
best fit line over here,

and if you trace that to the
vertical axis on the left,

you can see the price
is maybe around

here, say about $220,000.

This is an example of what's

called a supervised
learning model.

We call this supervised learning

because you are first
training a model by giving

a data that has right
answers because you get

the model examples of

houses with both the
size of the house,

as well as the price that

the model should
predict for each house.

Well, here are the
prices, that is,

the right answers are given

for every house in the dataset.

This linear regression model is

a particular type of
supervised learning model.

It's called regression model
because it predicts numbers

as the output like
prices in dollars.

Any supervised learning
model that predicts

a number such as 220,000 or

1.5 or negative 33.2

is addressing what's called
a regression problem.

Linear regression is one
example of a regression model.

But there are other models for

addressing regression
problems too.

We'll see some of those later in

Course 2 of this specialization.

Just to remind you,

in contrast with the
regression model,

the other most common type of

supervised learning model is

called a classification model.

Classification model predicts

categories or
discrete categories,

such as predicting if
a picture is of a cat,

meow or a dog,

woof, or if given
medical record,

it has to predict if a patient
has a particular disease.

You'll see more about

classification models later
in this course as well.

As a reminder about

the difference between
classification and regression,

in classification, there are

only a small number
of possible outputs.

If your model is recognizing
cats versus dogs,

that's two possible outputs.

Or maybe you're trying
to recognize any of

10 possible medical
conditions in a patient,

so there's a discrete,

finite set of possible outputs.

We call it classification

problem, whereas in regression,

there are infinitely
many possible numbers

that the model could output.

In addition to visualizing

this data as a plot
here on the left,

there's one other
way of looking at

the data that would be useful,

and that's a data table
here on the right.

The data comprises
a set of inputs.

This would be the
size of the house,

which is this column here.

It also has outputs.

You're trying to
predict the price,

which is this column here.

Notice that the horizontal
and vertical axes

correspond to these two columns,

the size and the price.

If you have, say, 47
rows in this data table,

then there are 47 of

these little crosses on
the plot of the left,

each cross corresponding
to one row of the table.

For example, the first row

of the table is a
house with size,

2,104 square feet,

so that's around here,

and this house is sold for
$400,000 which is around here.

This first row of
the table is plotted

as this data point over here.

Now, let's look at

some notation for
describing the data.

This is notation that you find

useful throughout your
journey in machine learning.

As you increasingly get

familiar with machine
learning terminology,

this would be
terminology they can

use to talk about machine
learning concepts

with others as well
since a lot of

this is quite
standard across AI,

you'll be seeing this notation

multiple times in
this specialization,

so it's okay if you don't

remember everything
for assign through,

it will naturally become
more familiar overtime.

The dataset that you
just saw and that is

used to train the model
is called a training set.

Note that your client's
house is not in

this dataset because
it's not yet sold,

so no one knows
what the price is.

To predict the price of
your client's house,

you first train your
model to learn from

the training set and
that model can then

predict your client's
houses price.

In Machine Learning, the
standard notation to denote

the input here is lowercase x,

and we call this
the input variable,

is also called a feature
or an input feature.

For example, for the first
house in your training set,

x is the size of the house,

so x equals 2,104.

The standard notation to denote

the output variable which
you're trying to predict,

which is also sometimes
called the target

variable, is lowercase y.

Here, y is the
price of the house,

and for the first
training example,

this is equal to 400,

so y equals 400.

The dataset has one row for

each house and in
this training set,

there are 47 rows with

each row representing a
different training example.

We're going to use
lowercase m to

refer it to the total number
of training examples,

and so here m is equal to 47.

To indicate the single
training example,

we're going to use the
notation parentheses x, y.

For the first training
example, (x, y),

this pair of numbers
is (2104, 400).

Now we have a lot of
different training examples.

We have 47 of them in fact.

To refer to a specific
training example,

this will correspond to

a specific row in this
table on the left,

I'm going to use the notation

x superscript in parenthesis,

i, y superscript
in parentheses i.

The superscript tells us that

this is the ith
training example,

such as the first,

second, or third up to the
47th training example.

I here, refers to a
specific row in the table.

For instance, here is
the first example,

when i equals 1 in
the training set,

and so x superscript 1 is

equal to 2,104 and y superscript

1 is equal to

400 and let's add this
superscript 1 here as well.

Just to note, this superscript i

in parentheses is
not exponentiation.

When I write this,

this is not x squared.

This is not x to the power 2.

It just refers to the
second training example.

This i, is just an index into

the training set and refers
to row i in the table.

In this video, you saw what
a training set is like,

as well as a standard notation

for describing
this training set.

In the next video,

let's look at what
rotate to take

this training set that
you just saw and feed it

to learning algorithm so that

the algorithm can
learn from this data.

Let's see that in
the next video.