OK so we have an idea of what machine learning is kind of.
But then there's other things.
Right.
Like A.I. Artificial Intelligence data science deep learning neural networks.
What are all those things.
Now let's look at machine learning and how it fits into some of the other words you may have heard.
Now you have to keep this diagram in mind.
You see it all starts with A.I. or artificial intelligence which simply means a human intelligence exhibited
by machines and A.I. is a machine that acts like a human.
And currently in our industry we have something called Narrow A.I. that is machines can be just as good
or even better than humans at specific tasks.
For example detecting heart disease from images or at a game of Go or chess or Starcraft and other video
games.
But each A.I. is only good at one task.
Narrow A.I. that we currently have simply means those machines can only do one thing really well they
can't be like humans and have multiple abilities.
That's called General A.I. and it's something that we're very very far away from now machine learning
is a subset of A.I. and machine learning is an approach to try and achieve artificial intelligence through
systems that can find patterns in a set of data.
And actually Stanford University describes machine learning as the science of getting computers to act
without being explicitly programmed that is getting machines to do things without us specifically saying
do this then do that.
If then if this if this then that and don't worry we'll explain that more throughout the section.
Now you may have also heard of deep learning and deep learning or deep neural networks is just one of
the techniques for implementing machine learning.
For now you can just think of it as a type of algorithm but then we have this other thing that we've
heard of right.
That's also very popular.
That is data science and often the role of a data science and machine learning expert are quite overlapping.
And most job descriptions actually don't even have a clear distinction between what is a machine learning
expert and a data science expert.
The field of data science simply means analyzing data looking at data and then doing something with
it usually some sort of a business goal.
So when we talk about machine learning there's a lot of overlap with data science and that's why this
course is called machine learning and data science.
If you are a data scientist you need to know machine learning.
If you are a machine learning expert you need to know data science so throughout the next couple of
videos we're going to be talking about both of these topics because both of them relate and overlap
and you can't do one without the other because you need to be able to understand data and work with
data to do any of these things.
That's why when we talk about careers often things like machine learning data scientist data analyst
they're all similar because they all work with data and from that data they want to derive some sort
of action for their business for the company or for their users.
Now in this course we're really focusing on the application side of things.
That is the day to day use of machine learning and data science.
We won't focus on theoretical academic research which means that you won't need a P H D Or be super
smart mathematician or statistician to do the scores.
The focus is on using machine learning and data science to be productive and to get job ready because
that's what companies want right now.
Now when Daniel starts introducing data science and machine learning he'll combine these two into one
and move this machine learning circle inside of data science.
That's because the goal of the course is to encompass this entire data science field and teach them
machine learning aspects within data science by the way you might be wondering hey what about this whole
data engineering thing that I've heard about.
That's another term isn't it.
What about that.
Talk about that Andre.
Well you know what.
We have a whole section on it later on in the course of for now pretend like it doesn't exist.
We'll get to it I promise.
So now that we have an idea of what these big words are let's have some fun in the next video.