Building on our understanding of vectors, we now turn to matrices. A matrix organizes numbers into a rectangular grid of rows and columns, providing a structured way to handle data and represent transformations common in machine learning.

This chapter covers:

• The definition of a matrix and its arrangement in rows and columns.
• Standard mathematical notation for matrices and specifying their dimensions (e.g., an $m \times n$ matrix).
• Creating and handling matrices in Python using 2D NumPy arrays.
• Identifying important special matrices: square, identity ($I$), zero ($O$), diagonal, and triangular matrices.
• Hands-on practice creating these different matrix types with NumPy.

By the end of this chapter, you will be comfortable defining matrices and representing them computationally, preparing you for the matrix operations discussed next.