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Welcome back.

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We're in a totally new section now which is very very exciting.

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And once again we'll start off reminding ourselves with the framework we're using for machine learning

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projects.

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We begin with problem definition.

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We look at what data we have.

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We define an evaluation metric for ourselves.

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We check out the features which one should we model.

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We figure out what kind of machine learning model we use.

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Now we run some experiments on seeing if we can improve things or try something else.

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And once again all of this is an iterative process and we've seen a few tools now that we can use for

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our framework which is is that pot lib num pie.

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But now I'm bursting at the seams here with excitement for this section why because we're gonna start

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getting into the machine learning of things writing machine learning code and to do so we're going to

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be using psychic loan.

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Now you might have a question what is psychic loan.

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Now I've put in brackets SCA loan because you'll often hear psychic loan and SBA loan used interchangeably

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and it's SBA loan is just the shortened version of psychic loan.

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So as a loan you say yes Kay loan or psychic loan you can think of them talking about the same thing.

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Well fundamentally psychic loan is a python machine learning library which means if we have data so

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I get loan helps us build machine learning models to make predictions or loan patterns within that data

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and then make predictions.

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And it also implements tools to help us evaluate those predictions are they good or are they bad.

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Are they worth holding onto.

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Could we put them into our application and actually use them now.

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Why psychic loan.

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Well it's built on num pie and map plot lib a couple of tools that we've seen.

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Oh and Python of course which is our language of choice.

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It has many built in or inbuilt machine learning models as we'll see.

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We'll get hands on with a few of them.

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And now this is important right because a lot of the time people get confused thinking they have to

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learn machine learning the math inside and out before they can start to use machine learning models

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but psychic line sort of abstracts away from that and lets us apply the benefits of machine learning

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to our problem straight away by leveraging the machine learning models it has inbuilt in the library.

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And it also has methods to evaluate such machine learning models so say we build one as we talked about

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before on our data for some sort of problem that we're working on.

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How do we know if that model has actually learned anything or if we did put it into production could

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be trusted in some way or another.

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And as a little bit of an aside it is a very well-designed API and API.

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Another word for just programming library it's like it.

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Once you get into it.

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Once you get hands on with it there's a lot there but you'll start to realize oh wow the people who

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have made this have put a lot of thought into it and made it beautiful So what are we going to cover.

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Well we devised a psychic learn workflow.

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What's this look like.

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Well that first step is to get our data ready.

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And the next is to pick a model to suit your problem.

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Now if you're wondering what is going on here with this web of circles and blobs and different colors

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and different shapes.

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Well don't worry we're going to see what that is.

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Just hold tight hold tight.

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I'm excited too.

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And then step three is we're going to fit the model.

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So once we've chosen a model using this web here we're going to fit that to our data and fit in psychic

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line is another word for a model.

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Find patterns within our data.

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And then once we've finished once it's found patterns within the data we'll use that trained model which

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is a no word for a machine learning model who has found patterns in data to make a prediction then we'll

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evaluate those predictions see if they're worth holding on to or maybe not.

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Maybe we need to improve through experimentation that might involve turning the knobs on some of our

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models or doing experiments.

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And then finally we'll see how to save and reload a trained model because sometimes machine learning

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or training machine learning models it can take a while for them to learn.

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And so it'd be helpful if we could save a model and then reload it so we could share it with a colleague

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or share our results online or put our model into an application of sorts and we wouldn't have to learn

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the patterns in data every single time that you used it.

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So this is what we'll be working through.

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And then more specifically we're going to do an end to end psychic loan workflow.

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And then these steps here are really diving deeper into the steps in that workflow that we just saw.

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So we're gonna start off with this we're going to first and foremost just go straight through this to

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begin with and then we're gonna break it down.

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So we'll have an end to end psychic learn workflow and we'll look at the step one getting data ready

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prepare it to be used with our machine learning models we'll see how to choose a machine learning model

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for what problem we're working on we'll learn how to fit a model to the data a.k.a. learning patterns

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we'll learn how to make predictions with a machine learning model okay using those patterns that it's

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learned and then we'll learn how to evaluate those predictions.

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So evaluating whether a machine learning model is learning patterns that are worthwhile.

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We'll see how to improve said predictions and then we'll finally finish up with saving and reloading

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models another important question where can you get help.

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Well as always follow along as best you can with the code we'll be going hands on in a Jupiter notebook.

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So if you can write the code if in doubt run the code.

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This is part of this step.

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Try it for yourself.

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Remember our motto if in doubt run the code if you want to find out more about what a particular function

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is doing.

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Don't forget to press shift in tab to read the doctoring.

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So when you're in the middle of function we'll see this in play when we're going through the notebook.

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You can see the doctoring.

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This will take you straight to the documentation if you're still stuck.

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You can search for it.

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And when you do search for a psychic line function such as how to evaluate a psychic line classification

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model you'll probably come along resources such a stack overflow or the user guide.

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And remember these resources the library is vast.

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So just be patient with searching for it.

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It does take me a while to figure things out too.

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I'm not perfect.

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It is okay to look something up and and try and figure it out for yourself.

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And then once you have search for it.

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If you do find something.

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Try again.

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And if you're still stuck finally it's okay to ask.

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Never be afraid to ask a question.

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So you exile as I am.

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Well if you are let's get into it let's get hands on let's build some machine learning models and let's

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get our cells familiar with psychic learn.