‫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, and then 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 pandas map pop lib num pi.
‫But now I'm bursting at the seams here with excitement for this section Y, because we're going to start
‫getting into the machine, learning of things, writing machine learning code.
‫And to do so we're going to be using CI kit learn.
‫Now you might have a question what is socket learn?
‫Now I've put here in brackets ask learn because you'll often hear psychic learn and scale learn used
‫interchangeably and it's scalar learn.
‫It's just the shortened version of psychic learn.
‫So ask learn.
‫If you see Skyline or Psychic Learn, you can think of them talking about the same thing.
‫Well, fundamentally, psychic learn is a python machine learning library, which means if we have data,
‫psychic learn helps us build machine learning models to make predictions or learn 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 on to?
‫Could we put them into our application and actually use them?
‫Now.
‫Why psychic alone?
‫Well, it's built on numpy 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 learn 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 in-built in the library,
‫and it also has methods to evaluate such machine learning models.
‫So so 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
‫we trust it in some way or another?
‫And as a little bit of an aside, it is a very well designed API and API.
‫In other words, for just programming library, it's like learn 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 loan workflow.
‫And so what's this look like?
‫Well, the first step is to get our data ready.
‫And the next is to pick a model to suit your problem.
‫And 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 loan is another word for a model.
‫Find patterns within our data and then once we fit it, once it's found, patterns within the data will
‫use that trained model, which is another 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 onto, or maybe not, whenever
‫we need to improve through experimentation.
‫And 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 would 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 going to start off with this.
‫We're going to, first and foremost, just go straight through this to begin with and then we're going
‫to break it down.
‫So we'll have an end to end psychic learn workflow.
‫Then 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 the machine learning model a.k.a 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 learnt 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 Jupyter 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 and out, 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 docstring.
‫So when you're in the middle of a function, we'll see this in play.
‫When we're going through the notebook, you can see the docstring.
‫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 loan function such as how to evaluate a psychic learn classification
‫model, you'll probably come along resources such as 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 to 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 searched 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 excited 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.