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