Hello and this will do.
We will create a very simple single person run more than to classify flower species depending on their
pattern length and pattern.
We will be using a skill to create this single person prone model.
In the latter part of this course we will use gave us and that flow to create mighty level parts of
models.
Skillern is a very popular machine learning library for Python it is the go to library to create regression
classification.
This year increase or as we are mortals we have separate lectures on all this machine learning models.
So if you are interested in learning any one of these you can go ahead and check out courses on regression
classification between trees and SVM so let's start first.
We will import numbers and find us.
Now if you have installed Anaconda there is no need to install a skill on separately.
You just have to import a scanner.
But in case you are facing any editing importing a skill you can install it using pip or Condi installed.
So just run this Come on pip install a Skillern in your command prompt you can open your command prompt
by pressing windows out and right CMB in the run come on and hit enter there in the command prompt you
can write this code and execute it to install that skill in or else you can directly run this code and
despite the notebook and this will also install escalation for you.
So if you have installed a skill on first We will load to Iris data.
There are various CSP files out there.
You can also import those CSC failed to load this data but as skilled on have some predefined data sets
and we will load our Iris data from there.
So just write from a scale on dot datasets import load underscored itis and then we are saving the word
Iris data into this variable Iris.
So just run this now.
Let's take a look at our data.
You can see there are four columns two columns that set a length separate by the length and patterns
with as I said earlier this data of different types of flowers.
There are three different types of flowers sir dosa What's the colour Virginia.
And for each of this flower we have their separate land separate but the length and petals.
So here we have these four variables and that category is stored in Iris dot target.
So in our example we want to create a perception model which would identify whether the flower is that
dosa or not using it and by land as the independent variables if you want you can take all the four
variables but for our example we are just taking these two variables to predict where the flower is
that shows up or not.
So we want our independent variable to be this by the lantern petals which we will store this information
in another variable which we are calling X. We are defining X as Iris dark data and here we just want
the third and fourth column.
That's why we have returned to common three.
If you remember the indexing it starts with 0.
So the last two columns are two and three.
Just run this so our x variable is now ready.
Now let's just look at the target variable here.
You can see we have different categories 0 1 and 2 0 percent for the set dosa one sentence for what
colour and 2 cents for Virginia.
Now if you have some machine learning knowledge you may know that to create classification more than
a what via variable should be in the form of 0 and 1.
So ideally we want one and all these records where the flower is set up and 0 and all these records
where the flower is virtually color or Virginia so let's just convert this target variable using some
basic operations first let's convert this target variable in the form of true and false we want crew
where the flower is set dosa that is the value of Target is 0 and we want false where the flower is
pathogenic or what color that is the numerical value is 1 or 2.
So let's just blend this Come on we are just checking whether the target is equal to zero or not if
the target is zero we will get group and if the target is not equal to zero we will get false let's
run this pain.
Now let's look at our way variable you can see we have converted zeros to crew and one or two boot false
now in the next step.
We want to convert this crew in falls to 1 and 0.
We want one in place of crew and we want zero in place of false.
So we will just use as pipe method and we will convert it to.
And so just on and look at our Y variable.
So as you can see now our Y variable is an array with 1 and 0 1 means that dosa and zero means not set
so
now let's look at our x variable before creating open it's up front more than you can see our x variable
is a two dimensional array with by the length and by the late
know over X and Y variable separately.
We just have to create our perception model and train that model using this x and y variables
we can create a single concept drawn more than using Eskil then.
But to create MLP on my level but it's up front more than we have to use k does an intensive look.
We will be looking at an LP in the later part of this scopes but now we will create this but it's up
grown model using Escalade only
now.
First we need to import spectrum from a scale undoubtedly no more than let's just import it and you
can also look at the documentation using this link.
This is department notebook is also shared in the resources section of this video.
So you can download it and learn it if you want to practice.
So this is the official document of Sep drawn in Eskil Skillern you can look at all the parameters which
we can give.
The first one is the penalty if you remember in linear regression there are regularization domes L1
a two also known as lasso and rich.
You can also give a fart that is the variable we use for regularization.
Then there are different other hyper parameters.
We will just stick to basic default high but parameters.
But you can look at it if you want.
These are just some very basic high but parameters that we get with most of the machine learning algorithms
now.
As with any other skill on machine learning model we first have to create an object of the Talbot item.
Then we have to fit over x and y variables and put that object and then we can use that object to predict
the future values of Y.
So first let's create an object that is perceptive on classifier we are naming all the variables but
underscored CnF and then we are using perception that we have just imported and we are giving on the
one hyper parameter that is random I say to equate to 42.
This is basically to reproduce the same result whenever we run this model.
So if you give random a set you will always going to get the same result.
Giving this hyper parameter is not mandatory.
You can skip that also if you want and in the next line we are fighting over x and y variables into
this object.
So let's fund this.
So what model is screened.
You can get the values of different types but I might add here.
If you want.
Now let's predict the value using the word classifier object we can just use dot predict my term.
So this is a word object and we are using dot product and we are giving our independent inquiry but
which is x as an input.
Let's do that.
You can see this are the predicted values.
We can compare this predicted values with the actual values.
There is no need to manually do that.
There is then another function that is accuracy scored available in Ashkelon which will give us the
accuracy for what prediction accuracy score vary between 0 and 1 0 means 0 percent accuracy which means
all the predictions are wrong and one means hundred percent accuracy which meant all the predictions
outright.
So let's just first and the accuracy score from a scalar not my kicks and then we are going to use accuracy
score.
And here there are two arguments.
First we have to provide the actual values and in the next type of humans we have to provide the predicted
values.
So what actual values are restored in just by a variable and the predicted values we have is stored
in y underscore right.
So let's get the accuracy squared off forward predictions.
So the accuracy here is one that is a hundred percent accuracy or what perception was able to identify
the specie of the float with 100 percent accuracy.
Now this is a very simple model.
Usually we don't use parts of upfront for any regression or classification tasks we usually go for machine
learning techniques where the data is not following any context pattern and we usually go for.
And that would be where the data is falling a very complex pattern.
Usually you will never find yourself using but it's a drawn.
And business settings but this is just center in production and we want you to give just some basic
idea about running but sap run models using ask them now after training your more than you will get
these two attributes that is the coefficient and then that Sept basically over perception as dividing
this is space using linear regression line.
So this is the coefficient and this is the intercept of that line.
So your equation of that line will be for minus one point four times the length minus two point two
times battle.
So if you want to cleared that line you can use coefficient and intercept values to do that and you
can also see the impact of our different variables on the Y variable.
So this coefficient is giving you the impact of each of those variables.
This is the impact of the land and this the impact of the.
That's all for this lecture.
Next we will look at tensor flow and get us to create our MLP model.
Thank you.