All right.
I think it's time for us to actually start practicing some of these skills instead of just talking about
theory, because I know right now everything looks super complicated.
You think that to be a machine learning expert, you have to have a PhD and you have to be really,
really smart with math and statistics.
But often that's not really the case.
So in this video, I want to show you what it actually means to do machine learning or data science.
So in this machine learning 101, let's talk about what a machine learning expert does.
The first thing that a person does is to usually import.
The data.
That is, we have some sort of a CSV file and we're going to import that data and we know how to do
that with Python, right?
We import a CSV data file so that we can use it in our program.
And don't worry, these are the steps that we're actually going to show you when we do our upcoming
projects.
So the first thing is to import the data.
Next, what do we do?
Well, we clean the data.
That is, in an ideal world, we have perfect data.
All the columns, all the rows of a CSV file are filled in and everything is perfect.
But that's often not the case.
Sometimes you have missing labels, sometimes you have missing cells or data, and sometimes we have
just unusable data that's really, really bad.
So we ideally clean the data so that when we use it later on, it's not going to give us any errors
or make bad predictions.
Now, number three.
What does a machine learning expert do?
Well, we split the data.
And this is an interesting one.
We usually split the data into what we call a training set.
And a test set.
What does this mean?
Well, splitting the data simply means just deciding.
We have, let's say, 100 rows of Excel and we decide that the 80 rows are going to be our training
set while we give the machine to learn from.
And then the last 20 rows are going to be the test set because we have to tell the machine whether it
got the answer right or wrong.
So we need a way for us to test with our pre existing data whether our model is good or bad.
Again, this is something that we'll explore, but usually you split your data, your Excel sheet into
a training set, what we use as input, and then the test set, what we use to test the output that
that machine learning function gives us.
And that's really easy to do.
We just decide, hey, 80% of the data is going to be training set and 20% is the test set up until
now.
These three things are pretty easy, right?
And as we'll see, they are quite easy.
Next is we create a model.
All right.
This one sounds really, really hard, right?
We just went from splitting data to all of a sudden we're creating this function, this model that our
program creates.
But the funny thing is, is that most likely what you do when you create a model is to import an algorithm.
You're not going to write the algorithm from scratch yourself.
Usually you use a library like Psych Learn, which we're actually going to use to simply do an import
and then decide what algorithm you want to use, like decision trees or K nearest neighbor.
And we'll actually see how to do that in an upcoming exercise.
So that's really nice.
Somebody has already built an algorithm for us that creates the model based on our training set.
And the running joke is that when people think about machine learning expert, they think of all these
mathematical equations that people do.
But most of the time it's people just simply importing from a library, an algorithm.
That's it, a one liner.
Next, what do you think we do?
Well, we check the output, right?
We create the model.
That is the function.
We have the input, which is the training set, and then we check the output of this model with our
test set.
So the test set, we're going to say, hey, is the output matching what we expected?
So once we check the output, what do we do?
Well, we improve.
So most likely the first time around, it's not going to get it completely right.
So based on the test set, we say, hey, we can probably improve something.
So as a machine learning expert, you might give it extra inputs.
That is maybe an extra column in the Excel file.
Or we might want to change the model or the algorithm that you used to create this model.
That's it.
These are the six steps of what you do, and you just repeat these steps.
Now we'll come back to these and I'll link to this in the resource so that you can always have it in
the back of your mind.
But right now, you just learn something really, really fundamental.
You see Facebook here.
This is pretty much how they run their machine learning pipeline.
And this diagram now should make sense just on those six steps that we talked about.
We define their goals.
That is, what do we want to accomplish?
We gather the data.
We parse the data.
That is, we clean it.
We split it into training data and testing data.
We then do model creation and we experiment with different algorithms, and then we finally test our
data, see how accurate it is based on our testing data.
And then we just go back and try different things until our model or our function is in line with what
we defined as a goal.
Isn't that cool?
You just learn how Facebook's machine learning structure works.
Now, let me ask you one question before I end this video.
What do you think the hardest part of this entire process is?
I made it sound very, very easy.
Right.
Think about it.
Ready for the answer.
The hardest part is actually grabbing the data because the more data we have, the better the more inputs
we have, the more training sets we can have so that we can improve the model.
And yes, we can decide what algorithm to use to create a model, what inputs to use during the training
set.
But the more data we have, the more options we have as machine learning experts.
And it's really tough to collect good data or what we call data set.
And companies like Google and Facebook are really, really valuable because they're the only ones that
can collect that much data.
And that's why machine learning is pioneered by these companies that have large sets of data.
Because you and I just simply don't have enough data to really make machine learning worth it.
Most companies don't actually need machine learning or can even implement good machine learning because
they don't have enough data.
You need big companies like Amazon to use machine learning because they're the only ones to have access
to that data.
And that's why they're so valuable.
They can share their model.
They don't care, but they usually keep their data private because that's what gives them a competitive
advantage.
All right.
So now we know the steps of machine learning.
The next is to learn what tools we can use to do these steps so that we can finally start learning and
building our own machine learning models.
I'll see you in the next one.
Buh bye.