Introduction

Have you ever been asked to take a look at some data and came up empty handed? Maybe you weren't familiar with the dataset, or maybe you didn't even know where to start. This may have been extremely frustrating, and even embarrassing, depending on who asked you to take care of the task.

You are not alone, and, interestingly enough, there are many times the data itself is simply too confusing to be made sense of. As you try and figure out what all those numbers in your spreadsheet mean, you're most likely mimicking what many unsupervised algorithms do when they try to find meaning in data. The reality is that many unprocessed real-world datasets may not have any useful insights. One example to consider is the fact that these days, inpiduals generate massive amounts of granular data on a daily basis – whether it's their actions on a website, their purchase history, or what apps they use on their phone. If you were to look at this information on the surface, it would be a big, unorganized mess with no hope of clarity. Don't fret, however; this book will prepare you for such tall tasks so that you'll never be frustrated again when dealing with data exploration tasks, no matter how large.

For this book, we have developed some best-in-class content to help you understand how unsupervised algorithms work and where to use them. We'll cover some of the foundations of finding clusters in your data, how to reduce the size of your data so it's easier to understand, and how each of these sides of unsupervised learning can be applied in the real world. We hope you will come away from this book with a strong real-world understanding of unsupervised learning, the problems that it can solve, and those it cannot.