‫Y'all ready for this?
‫We got step six, which is experimentation, and it'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 and another and another and just keep going through this loop.
‫Right.
‫But how would all of this look in practice?
‫Well, with a process like the one we've got here, machine learning projects become 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, okay,
‫I know what tools I can use for each of this step.
‫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 data set and ask you to find insights on it.
‫You might start by defining a problem with them, 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 and 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 five.
‫Now your first model goes pretty well.
‫We've got the little green tick here.
‫You've managed to line up the inputs.
‫So 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 asks 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 experimenting.
‫You might try a different model.
‫We've got model two and see how it goes and it performs pretty well.
‫Then if that didn't work when you reported it and it's like, Oh, maybe you can do a little bit better,
‫you might try vary the input 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 of lectures, you've just ticked off step one We've now got
‫a framework to use for the modelling section of machine learning projects.
‫Look how beautiful that is.
‫Step one problem.
‫Definition.
‫Step two Data.
‫Step three Evaluation.
‫Step four Features.
‫Step five Modelling.
‫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.