Machine learning often involves processing data arranged in tables or arrays. Linear algebra offers the mathematical framework for effectively handling these structures. At its core, data points, features, and model parameters frequently take the form of vectors (lists of numbers) and matrices (rectangular grids of numbers). Understanding linear algebra is key to grasping how many machine learning algorithms operate, from simple regressions to complex neural networks.

This chapter will introduce why linear algebra is relevant in the machine learning context. We will look at:

• How datasets and features are commonly represented using vectors and matrices.
• Examples of where linear algebra concepts appear within machine learning algorithms.
• A high-level view of the main topics covered in this course: vectors, matrices, and their operations.
• Instructions for setting up your Python environment, including the NumPy library, which we will use for computations throughout the course.