‫You might be thinking, why are we going through all these stamps?
‫I've got my computer.
‫Why do I have to download Mini Conda with Python and Conda and then install all these packages?
‫I just want to get started coding.
‫And so you should be.
‫It's good to hear you want to code.
‫We'll be doing lots of that soon.
‫But getting your computer and getting our computers set up with mini conda and Conda are important steps
‫which will save a lot of time in the future.
‫When you download Mini Conda and subsequently Conda, Conda gives you the ability to create environments.
‫You'll hear a lot of these in different projects that you're working on.
‫Let me give you an example.
‫A workflow for our Classifying Heart Disease project might involve starting with our computer and then
‫creating a project folder which contains the data we're using patient records and the tools we're using
‫for our machine learning project.
‫After a little bit of experience, we know there's a collection of tools we might want to use, such
‫as pandas and map plot lib for data analysis.
‫A collection of tools or packages is called an environment.
‫We use Conda to create the environment and then use it again to install different packages.
‫Data science and machine learning tools to it.
‫Then every time we work on our project site classifying heart disease, we use the tools within our
‫environment.
‫So let's walk through this.
‫You've got your computer.
‫You want to start a new project such as working on heart disease.
‫You might create just a separate folder, maybe on the desktop or something like that.
‫And then within that one single folder, you keep everything that you're going to use with that project
‫in that folder, such as the data, remember, the patient records could be the data and the conda environment
‫which are these are all the tools you're going to be working on to find insights on that data to try
‫and predict heart disease.
‫Conda is what you use to create this environment, install and update the tools that you need.
‫Now, why is this?
‫Why is this helpful?
‫Why would we do it in this kind of way?
‫Well, here's where it comes in handy.
‫Say, for example, you're working on your heart disease project since much of machine learning is experimental.
‫You might be using a number of different tools for a single project.
‫Now, what if someone else wanted to start working on what you're working on with you?
‫Instead of setting up their own workbench from scratch, Conda allows you to share with them the exact
‫same set of tools and packages you've been using so they can get started straight away.
‫This may not seem helpful if you're working largely by yourself at the moment, but I can assure you
‫it is one of the biggest problems when you start working on a team.
‫There would be days where me and my teammates, we would literally spend all day working on the same
‫project or trying to set up or trying to work on the same project.
‫But we couldn't because we ran into dependency issues.
‫A dependency issue is a term that's used when you don't have the right package for the problem that
‫you're working on, because this problem, this project, this classifying heart disease will be dependent
‫on these tools here.
‫And when we ran into dependency issues, it mean that we couldn't install some of these tools.
‫Conda helps to fix that dependency issue.
‫It ensures that you can create this environment that's easily shareable and updatable that you can share
‫with someone else.
‫So if you had multiple people on your team and you're all working on a similar project with similar
‫data and similar data science and machine learning tools.
‫The reason being we do this workflow is so that you can create a project folder, and then if you had
‫to share it with someone else's computer or someone else on your team, you could just send them the
‫project folder that contains the data and your conda environment and they would be able to get started
‫straight away.
‫Now again, if this seems like a lot and it is a lot to take in, we're going to have hands on practice
‫setting up a project folder as well as an environment for each of the hands on projects we're going
‫to cover throughout this course.