However, it is very important to understand the differences because that's going to help you understand how machine learning and data science fit into the entire world right now. So the best way to do it is to look at a Venn diagram.

I'm going to draw a few circles here so we're going to have one circle here. This is going to be called DS for data science. I'm going to do another circle here and this one's going to be BD for big data and then I'm going to draw a third one. Right here. And it's a little bit larger because it's going to touch all of the different elements. And this one's AI.

So this is our artificial intelligence circle. This is data science and this is big data now where does machine learning fit in with all of this? Well, machine learning touches each one of these components. And so if we were to draw where machine learning fits in it fits in about right here.

So this is machine learning. And let's see why it fits specifically in this circle.

It's inside of artificial intelligence it truly is a child technology or a set of technologies inside of AI. Because remember the definition of AI, Artificial Intelligence is us trying to mimic human behavior and intelligence inside of computers. That is what machine learning does. Now, it also touches some of these other technologies, in order for machine learning to work properly. You usually have to have a form of data science included in that.

So data science is very different in certain respects. Most data scientists are statisticians so they understand how data works. They're working with various mathematical formula. All day long and seen the way that one type of data may affect another and they create predictions and they use probabilities. So in order to have a machine learning algorithm that really operates intelligently it does need data science included inside of that.

Now, big data, big data is where you have very large amounts of data. Imagine that you are building some type of intelligent sales calculator to estimate what type of user is most likely to purchase a product. If you have millions and millions of records to look at for historical purchases and that would definitely be categorized as big data.

If you have access to that your machine learning algorithm is going to be much better then if you only had a few hundred records. The more data that you have typically as long as you formulate your algorithm and you keep your data organized properly you're going to be able to have a very intelligent machine learning algorithm.

and then AI has all of those predictive kinds of algorithms built into it. So if you're wanting to get into the machine learning space or you're at least interested in it which since you're taking this course I am assuming that you are then these are the various technologies that you're going to be working with.

You're going to be working with stats in data science. You're going to be working with large amounts of data and then inside of AI you're going to be building intelligent systems that can react and learn from past behavior in order to give predictions on future results.