All right now I know in the last lecture we said well we're going to I'm going to jump into two different
tools.
But I thought if we get into the next section I just want to really highlight that all the steps in
this process won't be possible without the right tools and the be a detailed section on each of these.
But it's a little sneaky preview.
Let's have a look at what we'll be using.
The first thing is getting your computer set up and ready to use tools that can be used for these different
steps.
Think of it as creating a work shed for Data Science and machine learning projects on your actual computer.
To do this we'll use Anaconda which is like the hardware store of data science and machine learning
tools.
So it all starts in the base.
We've got your computer here.
We're going to install Anaconda and then once you've got Anaconda getting all of these other machine
learning tools like Jupiter notebooks pi torch name pi map plot layer pandas psyche can't loan cat boost
ex G boost and tensor flow well some of these are a mouthful but once you've got Anaconda getting these
tools that we need to to apply to that process we've just created that framework we've created will
be an absolute breeze.
So you've probably got these at the moment your computer.
Next we'll have a detailed section on on most of these each of these because there's a lot of machine
learning tools out there.
But I want to make sure that you've got hands on knowledge with some practical ones that you can get
started using straight away.
So what will this look like if we wanted to attach it to our process.
Well to write Python code and communicating our work we're going to be using Jupiter notebooks and these
are also the same kind of file you'll find all of the projects are done in.
If you stick with it you're going to finish the course with a few of these containing real world machine
learning projects where you can show people what you've done for data analysis.
So this little section here and we've we've done some color coding here on this diagram.
We've got this whole whole thing here.
This whole modeling section of problem definition experiments and data analysis machine learning that
can be thought of as data science.
We've seen a similar graphic to this in the past but this is now mapping the tools which are these two
hour framework that we've designed before.
So for data analysis you'll be using things like pandas that plot lib and num pi.
That's step two three and four building machine learning models you'll use things like tensor flow pi
torch psychic learn ex G boost and cat boost.
And what's important to remember is your most important role will not be knowing all of the functions
of each of these single libraries off by heart.
It will be knowing which tool to use for what kind of problem.
Again that's why Step 1 is defining our problem so you can know I can draw out of my my tool bag here
which which is this I can apply these tools are these stamps and these tools for these stamps and it's
gonna be all done in Anaconda and Jupiter notebooks.
Sometimes you won't know the answer.
Sometimes I don't even know the answer but since I've had some practice I know where to look I've kind
of thought about these different things I've had my hands on practice with these different things so
I know where to come to look.
And eventually you will too.
That's what we're focused on.
Okay.
Okay that's enough.
You should be proud you've learned some of the most fundamental concepts in machine learning.
Of course there are many more and there's there's different ways to do the same thing.
But these are more than enough what we've covered so far to start becoming a machine learning practitioner.
Up next we're going to get Anaconda installed on your computer because if we look at the diagram before
everything starts with Anaconda once you have your computer you install Anaconda you get these tools.
So that's what we're going to do next let's do it.