‫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 and the frameworks and whatnot.
‫We're going to put them all together and we're going to make something of it.
‫We're going to 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 well, 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.
‫What structure is this?
‫Well, this is rows and columns or something like a pandas data frame or something like an Excel spreadsheet
‫or maybe a Google Sheets document.
‫And so with our 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 IDs, 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 going to 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 future 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 data set.
‫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 psych 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.
‫Well the tools we're going to focus on a pandas map plot lib num pi so I can't learn.
‫And Jupiter, at least for the first project, we've 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 and the steps you could take with
‫almost any machine learning project.
‫And so far, we've got our computer.
‫We've downloaded and installed many Conda.
‫We followed this.
‫But this is we're up to now.
‫So this is if we have our computer and we have Conda or Mini Conda.
‫We're going to start a new project and then create a project folder and within that folder will live
‫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 Jupyter Notebook.
‫And then within that workspace we can perform data analysis and manipulation using tools like numpy,
‫pandas, map, plot lib, and then we'll start to look at how we can build or use machine learning models
‫within cyclone.
‫So cyclone 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 will you get those extra resources if you are following along, try it for yourself or if you
‫are going along with it at a different time.
‫Make sure if in doubt run the code.
‫Right.
‫That's the motto here.
‫If and 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 docstring.
‫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've put both documentations here for Cyclone and pandas because we're going to be using a few tools
‫here.
‫So you might even have to look up the documentation for NumPy or something like that.
‫Another tool that we're using, maybe even map 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 ask.
‫So never be afraid to ask a question.
‫You might do this on the forum space or on the StackOverflow 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.