Hello and welcome back.
Today we are talking about the machine learning process.
As you will see from the practical tutorials of this course,
there's a specific step by step process that we typically always follow
when building machine learning models.
So let's have a look at what this process entails.
This process has three main steps.
The first one is called data preprocessing.
And here we import the data.
We clean the data and we split the data into training and test sets.
A quick note is that in this course, we won't be focusing a lot
on cleaning the data.
That's because our data is pre cleaned so that we can really hone
in our other skills related to machine learning, but bear in mind
that in real world situations, clean the data is quite an important step.
Next, we move on to modeling where we first build the model.
Then we train the model and we make the prediction.
So this is the fun part of machine learning.
And in this course you'll get a great exposure to several different models.
And you'll be able to practice your skills there.
And finally we move on to evaluations.
We will calculate some performance metrics and make a verdict about our model,
whether it's a good fitting model and if it works for our data or not.
And this is a very important step to make sure that the models
we built really serve the purpose that they're designed for.
So there we go.
That's the machine learning process.
And in this course, you'll definitely have lots of opportunities to get
some great hands on experience with it.
And I look forward to seeing you next time.
Until then, enjoy machine learning.