Holy moly.
We did it.
That wasn't too bad, was it?
I mean, there's a lot of new concepts, and machine learning is a big, big topic that could be a whole
course in its own.
But I hope I was able to narrow down the essence of machine learning so you understand what's happening
when people talk about ML.
We're simply doing these steps and we've learned about all of these steps so that now you can start
to improve on them and learn how to perhaps improve at each step of the way.
Now, I've created a little notebook here that you can actually follow.
I even have done some visualizations for you here using map plot lib to show you some of the iris data
set and kind of explore it on your own.
But the key here is that you use Jupyter notebooks to visualize data, to manipulate data, to clean
data.
And then slowly, as you start to learn about your data, you start making predictions to test your
data, to train your model on this data set and hopefully get it to the point where you can make some
important business decisions.
Now, this iris data set isn't necessarily useful to somebody like Amazon or Google, but using these
basic principles, you now have the ability to increase the ability of a model from just detecting flower
types to detecting all sorts of images.
Image recognitions, self-driving cars all use the same principles.
But at the end of the day, all we're doing is creating these models or functions by giving them inputs
and some of our outputs to see how good we can create that function.
And all we're doing is moving ourselves away from the function writers to simply the data aggregators
and the people that change the inputs here and there to try and make the function as good as possible.
Now the topic of machine learning is really, really vast.
There's different topics that we can explore, but the key thing is that there are a lot of tools,
and in order for you to do machine learning really well, you need big data sets.
So companies that we talked about like Microsoft, Amazon, Netflix can do machine learning really well
and invest a lot of money into machine learning because they have these data sets available.
Now you can create your custom models, but these big companies also offer things like retrain able
models.
That is very general models like the vision.
AutoML Vision that you can train, but they initially at least have created the base model for you or
you can even use the pre-trained models which essentially do everything for you.
It's that model that you can just plug and play and use and you get the benefit of all that data that
Google collected.
They just give you the model that you want to use.
You want to do translation?
Well, you just send them the text and they'll respond with the translated text for you.
Because remember what I said, the value is not necessarily in the model, but in the data that we use
in creating that model.
This was a big topic.
I hope your head doesn't hurt and if you don't get everything 100%, don't worry.
This is a big topic that is constantly evolving and there are so many advances happening every single
day that even the experts are overwhelmed.
The idea of this section was just to introduce you to the topic so that maybe if this pique your interest,
you can explore these topics on your own further along.
I'll leave some resources, some of my favorite resources for you, so that you can explore this a little
bit further.
But for now, good job getting this far.
I'm really proud of you.
I'll see you in the next one.
Bye bye.