Let's talk about specific machine learning algorithms. In the previous lecture in SC4x, the course covered the theory that underpins both supervised and unsupervised classification techniques. Today's excerpt is much more practical. Learn about how to do dimensionality reduction, as well as how to actually implement machine learning algorithms. Let's start with some motivating questions.

One of the things that we have to grapple with in machine learning is that we're dealing with large multi-dimensional data sets. And sometimes there are 10 or 20 or 30 different characteristics or features in our data sets that are independent input variables, and we need to understand them a little bit better. Ultimately, we need humans to be able to understand what the signal is and what is driving the signal. And so we do this thing called dimensionality reduction. So we'll talk about techniques for it and motivate it. The next question is how can we group records together that don't contain labels and form predictions using unsupervised classification.



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