Machine learning is rapidly changing our world. As the centerpiece of artificial intelligence, it is difficult to go a day without reading how it will lead us into either a techno-utopia along the lines of the Singularity, or into some sort of global Blade Runner-esque nightmare scenario. While pundits may enjoy discussing these hyperbolic futures, the more mundane reality is that machine learning is rapidly becoming a fixture of our daily lives. Through subtle but progressive improvements in how we interact with computers and the world around us, machine learning is progressively making our lives better.
If you shop at online retailers such as Amazon.com, use streaming music or movie services such as Spotify or Netflix, or have even just done a Google search, you have encountered an application that utilizes machine learning. These services collect vast amounts of data—much of it from their users—that is used to build models that improve the user experience.
It's an ideal time to dive into developing machine learning applications, and, as you will discover, Python is an ideal choice with which to develop them. Python has a deep and active developer community, many with roots in the scientific community. This heritage has provided Python with an unparalleled array of libraries for scientific computing. In this book, we will discuss and use a number of the libraries included in this Python Scientific Stack.
In the chapters that follow, we'll learn how to build a wide variety of machine learning applications step by step. Before we begin in earnest though, we'll spend the remainder of this chapter discussing the features of these key libraries and how to prepare your environment to best utilize them.
These are the topics that will be covered in this chapter:
- The data science/machine learning workflow
- Libraries for each stage of the workflow
- Setting up your environment
