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 learn 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 program in Python and tried 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.
Cat meows but then the computer asks you back what is a meal.
What is for 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.
