Hey there.
This is Future Daniel.
Why future Daniel.
Well because I've just finished off recording the last video for this entire section.
The psychic land section so you might see in an upcoming video I save to a list.
What we're covering.
Section by section an end to and workflow.
Getting the data ready.
Choosing the right estimated 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 a pretty big section.
So if I scroll through the entire notebook all the code that we're gonna cover there is a lot here.
Right.
Right through.
Keep following through this is don't worry.
This is across the next 25 30 or so videos that you're gonna 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'll 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 ones 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 were 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 I can see 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 Kino we went through step one we're going to get the data ready we're gonna find out how
to pick a cyclone model we're going to fit model system data so we can make a prediction we're gonna
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 they'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 psychic line 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 to 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 psychic machine learning
map you'll start to see ah 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 in 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 predict the probabilities is done with predict
probe.
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 socket loan 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 and our model and
improving it.
Just like if we wanted to adjust our oven and while 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 are slightly you might get a better model.
So we'll see that in Section 5.
Then in section six we'll see how to save and reload our trained models so we can avoid going through
all the previous steps before if we wanted to save and share our model.
And then in Step 7 which is kind of a bonus which is not pictured in the workflow is we're gonna 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 gonna 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 and 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.