Now up to this point in the course, we've talked about Python careers and we've talked about being
a back in developer using Python, about testing an automation, about being a general Python developer
and doing scripting, being a software engineer and just writing software using Python.
But we haven't really touched on the subject of machine learning or research analyst.
Data Scientist.
Data analyst.
And you see all these fields kind of overlap.
So what is machine learning?
When we talk about machine learning, 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.
An A.I. is, well, 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 a specific task.
For example, detecting heart disease from images or at the game of go or even video games.
But narrow A.I. that we have currently 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 AI, 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, then
do this.
And don't worry, we'll explore 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 thing of data science, and often the role of a data science and a 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.
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.
And that's why when we talk about careers, often things like machine learning, data scientist, data
analyst or research analyst are similar because they all work with data.
And from that data they want to derive some sort of an action for the business, for the company or
for the person.
All right.
That's enough for now.
Let's move on to the next video.