‫Hey there.
‫This is future, Daniel.
‫Why a future, Daniel?
‫Well, because I've just finished off recording the last video for this entire section.
‫The psychic loan section.
‫So you might see in an upcoming video I saved to a list what we're covering section by section and end
‫to end workflow.
‫Getting the data ready.
‫Choosing the right estimate slash machine learning algorithm for our problems.
‫Fitting the model algorithm to the data and using it to make predictions and a few more steps.
‫And what you'll probably quickly realize is that this is a pretty big section.
‫So if I scroll through the entire notebook, all the code that we're going to cover, there is a lot
‫here, right?
‫Right through.
‫Keep following it through.
‫This is don't worry, this is across the next 25, 30 or so videos that are going to go through.
‫And here's that list that I created right at the start.
‫Right.
‫I think this is an in an upcoming video and now seeing this to start out with can be pretty intimidating.
‫That's what I want to sort of warn you, right.
‫Is that because there is so much because we are going through a lot of different concepts of not only
‫machine learning but psychic learn as itself.
‫I wanted to make an auxiliary resource that you can kind of come to and get a quick overview of what
‫we're doing.
‫So this list, although it tells you what's happening.
‫It doesn't really tell you much other than just the headings of the different sections.
‫So that's why there's resource that will be available in the resources section.
‫There'll be some sort of link in a place that you can find it.
‫So don't worry.
‫This is what we're covering in the cyclone introduction.
‫So this is kind of like a table of contents for this massive notebook that we're going to go through.
‫And don't worry, even once going through the entire cyclone section.
‫You're probably not going to know all this off by heart.
‫And if it seems like I know all this off by heart and I'm going through it, I'm seeming like, wow,
‫I could never do that.
‫Don't be mistaken.
‫I've had a fair bit of practice with the Cyclone Library, and that's why I'm able to go through and
‫sort of figure out things and know where to look and go to the documentation and go, okay, I need
‫this function to do that, this function to do that.
‫So going through it for the first time, remind yourself learning something new takes time and learning
‫anything worthwhile definitely takes time, especially machine learning.
‫And so this is the framework that we're going to be using for the introduction.
‫We saw this in the previous video, that keynote we went through.
‫So step one, we're going to get the data ready.
‫We're going to find out how to pick a cyclone model.
‫We're going to fit models with some data so we can make a prediction.
‫We're going to evaluate our model, see if it's worthwhile even using the model that we've got.
‫You'll see there's a couple of different ways to use this.
‫Depending on the model we're using will improve through experimentation because after all, machine
‫learning is all about experimentation, and the quicker you can reduce your time between experiments,
‫the more you're going to get done, the more insights you're going to figure out about your data, about
‫your machine learning models.
‫And then we're going to look at saving and reloading trained machine learning models so you don't have
‫to go through all these steps again.
‫You can save a model, send it to a friend, send it to a colleague, put it in your application and
‫use it.
‫That was a bit of a mouthful in itself.
‫But if you go through this resource, you'll see that I've got at the top standard library imports treat
‫this as like the mini version of this.
‫So this is the big behemoth that we're about to go through in this section through all 30 or so videos.
‫But this is like the mini overview you can quickly refer to, so you're not scrolling back and forth
‫through all the stuff that's going in here.
‫So to begin with, we've got some standard library imports.
‫We're going to see how we can import data sets to use examples in the sidekick loan.
‫We're going to focus on a classification and a regression problem using our heart disease and Boston
‫data sets.
‫Step one is getting the data ready, right?
‫So each of these sections refers to a little step in this workflow.
‫Get the data ready.
‫You'll see the functions that we use there.
‫You'll see section two is how we pick a model, so how we choose a machine learning model for what problem
‫we're working on.
‫This is one of the toughest things that you'll see in machine learning problems.
‫But again, once we get familiar with this diagram, the second line machine learning map, you'll start
‫to see, Oh, it's actually not as hard as you once might have thought.
‫And then again, there's some code that you might use for choosing a model and instantiating one.
‫Then there's some code here.
‫You'll see the functions that we use to fit a model to training data to make predictions.
‫We can view our predictions slash predicted probabilities.
‫Predicted probabilities is done with predict prober.
‫And if we come back down here, we'll see how we can evaluate our model.
‫And there's a link here.
‫There's a you'll see in the documentation.
‫There's a few different ways to evaluate different psychic learn models.
‫This is a great section.
‫I highly suggest reading through here because it's one thing to train a machine learning model.
‫It's another thing to check if it's doing the right thing.
‫What you want it to do.
‫Once we've gone through evaluating a model, we'll see how we can improve our models through experimentation.
‫What we're going to focus on is hyper parameter tuning, a.k.a turning the settings on our model and
‫improving it.
‫Just like if we wanted to adjust our oven and while our oven was cooking our favorite meal, maybe it
‫doesn't do so well on 180 degrees, but it does really well on 200 degrees.
‫The same thing goes with machine learning models out of the box.
‫It might work pretty well, but if you adjust the settings a slightly, you might get a better model.
‫So we'll see that in section five.
‫Then in Section six will see how to save and reload our trained model so we can avoid going through
‫the previous steps before if we wanted to save and share our model.
‫And then in step seven, which is kind of a bonus, which is not pictured in the workflow, is we're
‫going to put all of the above steps together in a single cell.
‫So that looks like a lot of code at the moment.
‫But don't worry, we're going to step through it.
‫By the time you're at the end of this section, none of this will be unfamiliar.
‫So with that being said.
‫Get yourself ready.
‫Get yourself excited.
‫Pour yourself a cup of coffee or go take a little walk or something.
‫Get yourself in the mindset to learn machine learning, because that's what we're going to do.
‫We're going to get into the psychic loan library and all of its magical goodness.
‫So I'll see you in the next video.