Oh, boy.
Here it is.
The coolest sounding section in the entire course.
Machine learning.
Now, throughout the course, we've learned that machines are really, really good at certain things.
Machines can perform tasks really, really fast.
And we learned through Python that we can control these machines and get them to do tasks for us.
Now, these machines or computers are really good at things that we can describe, right?
That we can write in code if else blocks to do something.
Computers are really good at working on tasks that have these defined rules all the way up to a game
of chess.
Right?
When you play a game of chess against a computer, we could technically have, if else blocks lots of
them to see how to move each piece and what the pros and cons are.
But then as soon as we stop having these specific rules, things start to break down.
For example, what is a cat?
If you could describe that to a computer or you had to programming Python and try to write that code
in Python, would you be able to?
I mean, sure, you can say, hey, a cat has fur, a cat has whiskers, a cat meows, but then the
computer asks you back, What is a meow?
What is fur?
The harder things become to describe.
The harder it is for us to tell machines what to do.
In this section.
We're going to tackle this problem and we tackle this problem using something called machine learning.
And you've definitely heard of it because it is a big buzzword right now in our industry because with
machine learning you have so many applications.
We could have self-driving cars, robots, vision processing, language processing, recommendation
engines, translations, stock price predictions.
There's so many applications.
The goal of machine learning is to make machines act more and more like humans, because the smarter
they get, the more they help us humans accomplish our goals.
Now in this section, we're going to go over some theory to get us familiar with this topic and then
do three projects on both data science and machine learning.
To understand this topic a little bit more, don't worry, don't get intimidated.
We're going to try and simplify things and have fun along the way.
Let's get started.