Machine Learning Algorithms
We will start by going through machine learning
algorithms one at a time. We will begin with one of the
most basic algorithms called linear regression. We will
try to understand the concept of correlation and
gradually move to simple regression. We will then dive
deeper into more and more aspects of simple
regression. In the next part of the course, we will learn
other advanced algorithms such as linear regression and
machine learning. We will go through one of these
algorithms one step at a time and then learn how to
improve our simple regression model. So without further
ado, let's dive into correlation.
What is Correlation?
Correlation is a statistical measure that shows the
extent to which two or more variables are related. We
will try to explain the meaning of correlation and its
importance in this part. We will also learn how to use
our own machine learning algorithm to understand more
about advanced algorithms for building a new machine
learning model.
Let's take the third example -- what happens to sweater
sales with an increase in temperature? Well, with an
increase in temperature, sweater sales go down. So,
there is a strong association, but it's a negative
association. Our correlation coefficient in this scenario
would be negative.
In the next step, we will try to understand how to
calculate correlation coefficient in Python in our Spider
coding environment. We will use Python for the next
step to calculate a negative correlation. We will also
look at the data to see how to use it from our Spider
code. We need to understand the data and see what it
means in our own version of the data. We've seen the
data that we used to see the data in a different way to
see. We use it in a new way to test the data for a new
version.
We will start by going through machine learning
algorithms one at a time. We will begin with one of the
most basic algorithms called linear regression. We will
try to understand the concept of correlation and
gradually move to simple regression. We will then dive
deeper into more and more aspects of simple
regression. In the next part of the course, we will learn
other advanced algorithms such as linear regression and
machine learning. We will go through one of these
algorithms one step at a time and then learn how to
improve our simple regression model. So without further
ado, let's dive into correlation.
What is Correlation?
Correlation is a statistical measure that shows the
extent to which two or more variables are related. We
will try to explain the meaning of correlation and its
importance in this part. We will also learn how to use
our own machine learning algorithm to understand more
about advanced algorithms for building a new machine
learning model.
Let's take the third example -- what happens to sweater
sales with an increase in temperature? Well, with an
increase in temperature, sweater sales go down. So,
there is a strong association, but it's a negative
association. Our correlation coefficient in this scenario
would be negative.
In the next step, we will try to understand how to
calculate correlation coefficient in Python in our Spider
coding environment. We will use Python for the next
step to calculate a negative correlation. We will also
look at the data to see how to use it from our Spider
code. We need to understand the data and see what it
means in our own version of the data. We've seen the
data that we used to see the data in a different way to
see. We use it in a new way to test the data for a new
version.
