Welcome back.
And I couldn't be more excited for this section.
You know why.
Because we're going to finally start getting hands on with an end to end project putting all the tools
that we've learned so far on the processes in the frameworks and whatnot.
We're gonna put them all together.
We're gonna make something of it we're gonna work on a project specifically with structured data or
a couple of projects actually.
And so you might be asking what is structured data.
And while we've actually been hands on with structured data throughout what we've learned so far basically
structured data is whatever you can fit into this sort of structure more structure is this.
Well this is rows and columns or something like a panda's data frame or something like an Excel spreadsheet
or maybe a Google Sheets document.
And so without heart disease data set that we've been working with as kind of a demo on the left hand
side you could have something like patient I.D. and in the middle you might have some feature variables
such as details about the patient their weight their sex heart rate chest pain and then on the right
hand side or somewhere in the data set.
You may have a target variable.
The thing you want to predict and that's in essence what we're trying to do with machine learning we're
trying to learn some patterns in feature variables and how those patterns relate to a target variable.
And that's what we're gonna do throughout this section is work on structured data projects which take
this sort of approach getting a machine learning model to learn patterns within feature variables and
then using those patterns to predict some target variable now how are we going to do that.
Well we're going to put this bad boy into play.
We've seen it a few times already.
We'll go through it step by step.
The data modeling section of a machine learning project.
So this is where we already have some data.
We're going to go through we're going to define what our problem is.
What problem are we trying to solve.
Is it classification is it regression.
Is it something else.
What data do we have.
We'll look at the dataset where might that actually come from.
What defines success for us.
So if we're working through a machine learning project and we're working on machine learning proof of
concept what level of success do we have to get to before we can say Okay this may move from proof of
concept into actually being deployed what features should we model.
So we have a look again back at the data and are there certain features of the data that should take
more precedence over others.
What kind of model should we use.
So we looked at this a little bit in psychic learn of how to choose a different machine learning model
for different problems and then finally what experiments could we try.
So what have we tried.
What else can we try and the whole thing here.
This is an iterative process that's the main thing to remember.
And then again if we go here we've got some tools that we can use for each step of the pipeline so if
this whole overall pipeline is data science all the tools we're going to focus on are Panda's map plot
lib num pi psychic line and Jupiter.
At least for the first project we got some green boxes highlighting the ones that we're going to be
focused on and we've already been hands on with some of these and we've seen what they can do.
But now it's time to combine them all in an overall project setting and there's one more diagram we
have to look at.
Well this is the steps we're going to take to set up a new project the steps you could take with almost
any machine learning project.
And so far we've got our computer We've downloaded installed many conduct.
We followed this but this year we're up to now.
So this is if we have our computer and we have Condor or mini Condor we're gonna start a new project
and then create a project folder and within that folder will leave some data.
We're going to create an environment similar to the one we've already been working in.
And then within that environment we can create our workspace in the form of a Jupiter notebook.
And then within that workspace we can perform data analysis and manipulation using tools like num pi
panders and map plot lib and then we'll start to look at how we can build or use machine learning models
within psychic learn so psychic line has those pre-built machine learning models that we can use.
We're going to see how we can apply those to our problem.
So you might be wondering of course where can you get help.
And the first step is to because we're going to be working through this project together is to follow
along with the code if you can.
If you fall behind don't worry it will be available to you through some sort of resource through some
sort of link in the place where you get those extra resources.
If you are following along try it for yourself or if you're aren't going along with it at a different
time.
Make sure if in doubt run the code right.
That's the motto here.
Find out run the code if you're curious about what a function does.
Remember you can press shift tab within the brackets of a function to read the dock string that will
give you a brief overview of what the function does and then don't be afraid if these three steps don't
work.
Don't be afraid to search for it right.
You'll probably end up in places like stack overflow or the documentation I put both documentations
here for cyclone and panders because we're gonna be using a few tools here.
So you might even have to look up the documentation for num Pi or something like that.
Another tool that we're using maybe even that plot lib.
But wherever you get stuck Don't be afraid to search for it.
What separates a good data scientist from a from a poor one is one that doesn't ask questions.
So a good data scientist is just the one that keeps asking more and more questions a good machine learning
engineer is just the same as a as a normal machine learning engineer.
But they ask more questions and this is where the try again comes in.
So once you've searched for it once you've figured out something once you've read something don't be
afraid to take it back to your notebook and try it.
And then finally as I said before what separates a poor machine learning engineer from a good machine
learning engineer is the questions that they are had never be afraid to ask a question you might do
this in the forum space or in the stack overflow section wherever you see fit.
So without any further ado let's jump in.
I'm really excited I hope you are.
Let's get our hands on our first end to end machine learning project.