Y'all ready for this.
We've got step six which is experimentation.
That's important to remember all of these steps here are a highly iterative process meaning you might
try one thing find out it doesn't work then try another another and another and just keep going through
this loop right.
But how would all of these look in practice.
Well with a process like the one we've got here.
Machine learning projects become a tool matching projects and that's what we're going to do.
We're going to use this process and learn the different tools for each step.
So eventually you'll be able to start to look at a machine learning problem and think about OK I know
what tools I can use for each of these STEM.
We can break it down.
I can use this for that for that and apply this process to whatever kind of problem you're working on.
For example someone might come to you with a dataset and ask you to find insights on it.
You might start by defining a problem within which is step one.
Then you go through to step two and look at the data.
This part is called data analysis.
Here you might define an evaluation metric based on the problem at the same time as inspecting the different
features of the data which is step three step four.
Once you know a little bit more about the data you decide to build a machine learning model using the
features you found to predict some target.
Now this is Step 5.
Your first model goes pretty well.
We've got a little grain taking here you've managed a lot of the inputs you've got a good model you've
matched the model to the problem and it's producing some some good outputs so you decide to report what
you found.
And after your initial report your Project Manager asked to see if the model can be improved to get
better results.
So you do your research and find there's another approach you can take.
This is step six experiment.
You might try a different model.
We've got model to see how it goes and it performs pretty well then.
If that didn't work when you reported it it is like oh maybe you could do a little bit better.
You might try vary the inputs slightly and change your desired outputs and try a new model again.
Don't worry if all of this looks confusing we're going to have plenty of practice doing this with hands
on projects throughout the course.
Remember at the start how we said there were three major steps we were going to do one create a framework
to match the framework to data science and machine learning tools and three learn by doing well.
After going through the last couple elections you've just ticked off.
Step one.
We've now got a framework to use for the modeling section of machine learning projects.
Beautiful lenses.
Step one problem definition.
Step two data.
Step three evaluation step four features step forward modeling Step Six experimentation all of this
is an iterative process.
Next up we're going to learn what tools we can use to apply the framework to different projects.
And of course to do all this we're going to be working hands on writing machine learning code and creating
projects of our own and remember to check the resources section for anything extra you may need.
You can find all of what we've talked about there.