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Hello and welcome back.

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Today we are talking about the machine
learning process.

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As you will see from the practical
tutorials of this course,

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there's a specific step by step process
that we typically always follow

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when building machine learning models.

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So let's have a look
at what this process entails.

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This process has three main steps.

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The first one is called data
preprocessing.

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And here we import the data.

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We clean the data and we split the data
into training and test sets.

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A quick note is that in this course,
we won't be focusing a lot

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on cleaning the data.

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That's because our data is pre cleaned
so that we can really hone

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in our other skills related to machine
learning, but bear in mind

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that in real world situations, clean
the data is quite an important step.

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Next, we move on to modeling
where we first build the model.

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Then we train the model
and we make the prediction.

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So this is the fun
part of machine learning.

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And in this course you'll get a great
exposure to several different models.

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And you'll be able to practice
your skills there.

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And finally we move on to evaluations.

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We will calculate some performance metrics
and make a verdict about our model,

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whether it's a good fitting model
and if it works for our data or not.

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And this is a very important step
to make sure that the models

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we built really serve the purpose
that they're designed for.

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So there we go.

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That's the machine learning process.

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And in this course, you'll definitely
have lots of opportunities to get

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some great hands on experience with it.

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And I look forward
to seeing you next time.

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Until then, enjoy machine learning.