 Let's finish our data science methods overview by getting a brief overview of machine learning. Now I got to admit, when you say the term machine learning, people start thinking about something like the robot overlords are going to take over the world. That's not what it is. Instead, let's go back to our Venn diagram one more time. And in the intersection at the top between coding and stats is machine learning, or as it's commonly called is just ML. And the goal of machine learning is to go and work in a data space. So you can, for instance, take a whole lot of data, we've got tons of books here. And then you can reduce the dimensionality, that is, take a very large scatter data set and try to find the most essential parts of that data. And then you can use these methods to find clusters within the data, like goes with like, you can use methods like K means you can also look for anomalies are unusual cases that show up in the data space. Or if we go back to categories, again, I talked about like for like, you can use things like logistic regression or K nearest neighbors K and N, you can use naive bays for classification or decision trees or SVM, which is support vector machines, or artificial neural nets, any of those will help you find the patterns and the clumping in your data so you can get similar cases next to each other and get the cohesion that you need to make conclusions about these groups. Also, a major element of machine learning is predictions, you're going to point your way down the road. The most common approach here, the most basic is linear regression, multiple regression, there's also Poisson regression, which is used for modeling count or frequency data. And then there's the issue of ensemble models where you create several models, and you take the predictions from each of those, and you put them together to get an overall more reliable prediction. Now, I'll talk about each of these in a little more detail in later courses. But for right now, I mostly just want to know that these things exist. And that's what we mean when we refer to machine learning. So in some machine learning can be used to categorize cases and to predict scores on outcomes. And there's a lot of choices, many choices and procedures available. But again, as I said, with statistics, and I'll say again, many times after this, no matter what, the goal is not that I'm going to do an artificial neural network or an SVM. The goal is to get useful insight into your data, machine learning is a tool and use it to the extent that it helps you get that insight that you need.