You might be thinking why are we going through all these stamps.
I've got my computer.
What do I have to download.
Many condo with Python and then and an old install 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 conduct are important stamps which will save a lot of time in the future when you download
many Conda and subsequently Conda kinda gives you the ability to create environments.
You'll hear a lot of these in 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 matte plot lib for Data Analysis a collection of tools or packages is called an environment.
We use content 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 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 gonna use with that project in
that folder such as the data.
Remember the patient records could be the data and the content 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 conduct
is what you use to create this environment.
Install and update the tools that you need.
Now why is this why is is 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.
Kondo helps to fix that dependency issue.
It ensures that you can create this environment that's easily shareable and update able 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
conduit environment and they would be able to get started straight away.
Now again if this sounds like a law 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.