This chapter introduces Scikit-learn, a fundamental library for machine learning in Python. We will begin by positioning Scikit-learn within the Python scientific computing environment and guiding you through the installation process.

You will learn about the library's consistent API design, focusing on its core components: Estimators, Predictors, and Transformers. We'll cover how data should be structured for use with Scikit-learn, typically using NumPy arrays or Pandas DataFrames. Finally, you'll see how to load the built-in datasets provided by the library, which are useful for practice. The chapter concludes with a hands-on exercise to verify your setup is working correctly.