Welcome back.
We're in a totally new section now which is very very exciting.
And once again we'll start off reminding ourselves with the framework we're using for machine learning
projects.
We begin with problem definition.
We look at what data we have.
We define an evaluation metric for ourselves.
We check out the features which one should we model.
We figure out what kind of machine learning model we use.
Now we run some experiments on seeing if we can improve things or try something else.
And once again all of this is an iterative process and we've seen a few tools now that we can use for
our framework which is is that pot lib num pie.
But now I'm bursting at the seams here with excitement for this section why because we're gonna start
getting into the machine learning of things writing machine learning code and to do so we're going to
be using psychic loan.
Now you might have a question what is psychic loan.
Now I've put in brackets SCA loan because you'll often hear psychic loan and SBA loan used interchangeably
and it's SBA loan is just the shortened version of psychic loan.
So as a loan you say yes Kay loan or psychic loan you can think of them talking about the same thing.
Well fundamentally psychic loan is a python machine learning library which means if we have data so
I get loan helps us build machine learning models to make predictions or loan patterns within that data
and then make predictions.
And it also implements tools to help us evaluate those predictions are they good or are they bad.
Are they worth holding onto.
Could we put them into our application and actually use them now.
Why psychic loan.
Well it's built on num pie and map plot lib a couple of tools that we've seen.
Oh and Python of course which is our language of choice.
It has many built in or inbuilt machine learning models as we'll see.
We'll get hands on with a few of them.
And now this is important right because a lot of the time people get confused thinking they have to
learn machine learning the math inside and out before they can start to use machine learning models
but psychic line sort of abstracts away from that and lets us apply the benefits of machine learning
to our problem straight away by leveraging the machine learning models it has inbuilt in the library.
And it also has methods to evaluate such machine learning models so say we build one as we talked about
before on our data for some sort of problem that we're working on.
How do we know if that model has actually learned anything or if we did put it into production could
be trusted in some way or another.
And as a little bit of an aside it is a very well-designed API and API.
Another word for just programming library it's like it.
Once you get into it.
Once you get hands on with it there's a lot there but you'll start to realize oh wow the people who
have made this have put a lot of thought into it and made it beautiful So what are we going to cover.
Well we devised a psychic learn workflow.
What's this look like.
Well that first step is to get our data ready.
And the next is to pick a model to suit your problem.
Now if you're wondering what is going on here with this web of circles and blobs and different colors
and different shapes.
Well don't worry we're going to see what that is.
Just hold tight hold tight.
I'm excited too.
And then step three is we're going to fit the model.
So once we've chosen a model using this web here we're going to fit that to our data and fit in psychic
line is another word for a model.
Find patterns within our data.
And then once we've finished once it's found patterns within the data we'll use that trained model which
is a no word for a machine learning model who has found patterns in data to make a prediction then we'll
evaluate those predictions see if they're worth holding on to or maybe not.
Maybe we need to improve through experimentation that might involve turning the knobs on some of our
models or doing experiments.
And then finally we'll see how to save and reload a trained model because sometimes machine learning
or training machine learning models it can take a while for them to learn.
And so it'd be helpful if we could save a model and then reload it so we could share it with a colleague
or share our results online or put our model into an application of sorts and we wouldn't have to learn
the patterns in data every single time that you used it.
So this is what we'll be working through.
And then more specifically we're going to do an end to end psychic loan workflow.
And then these steps here are really diving deeper into the steps in that workflow that we just saw.
So we're gonna start off with this we're going to first and foremost just go straight through this to
begin with and then we're gonna break it down.
So we'll have an end to end psychic learn workflow and we'll look at the step one getting data ready
prepare it to be used with our machine learning models we'll see how to choose a machine learning model
for what problem we're working on we'll learn how to fit a model to the data a.k.a. learning patterns
we'll learn how to make predictions with a machine learning model okay using those patterns that it's
learned and then we'll learn how to evaluate those predictions.
So evaluating whether a machine learning model is learning patterns that are worthwhile.
We'll see how to improve said predictions and then we'll finally finish up with saving and reloading
models another important question where can you get help.
Well as always follow along as best you can with the code we'll be going hands on in a Jupiter notebook.
So if you can write the code if in doubt run the code.
This is part of this step.
Try it for yourself.
Remember our motto if in doubt run the code if you want to find out more about what a particular function
is doing.
Don't forget to press shift in tab to read the doctoring.
So when you're in the middle of function we'll see this in play when we're going through the notebook.
You can see the doctoring.
This will take you straight to the documentation if you're still stuck.
You can search for it.
And when you do search for a psychic line function such as how to evaluate a psychic line classification
model you'll probably come along resources such a stack overflow or the user guide.
And remember these resources the library is vast.
So just be patient with searching for it.
It does take me a while to figure things out too.
I'm not perfect.
It is okay to look something up and and try and figure it out for yourself.
And then once you have search for it.
If you do find something.
Try again.
And if you're still stuck finally it's okay to ask.
Never be afraid to ask a question.
So you exile as I am.
Well if you are let's get into it let's get hands on let's build some machine learning models and let's
get our cells familiar with psychic learn.