Before we begin exploring unsupervised learning algorithms in detail, we will review how to set up and manage machine learning projects, covering everything from acquiring data to building and evaluating a model and implementing a solution. We will work with supervised learning models in this chapter—an area most readers should have some experience in—before jumping into unsupervised learning models in the next chapter.
Let’s set up the data science environment before going further. This environment is the same for both supervised and unsupervised learning.
These instructions are optimized for the Windows operating system but installation packages are available for Mac and Linux, too.
If you have not already, you will need to install Git. Git is a version control system for code, and all the coding examples in this book are available as Jupyter notebooks from the GitHub repository. Review Roger Dudler’s Git guide to learn how to clone repositories; add, commit, and push changes; and maintain version control with branches.
Open the command-line interface (i.e., command prompt on Windows, terminal on Mac, etc.). Navigate to the directory where you will store your unsupervised learning projects. Use the following prompt to clone the repository associated with this book from GitHub:
$ git clone https://github.com/aapatel09/handson-unsupervised-learning.git ...