To effectively apply the concepts of linear algebra, especially when dealing with the data structures used in machine learning like vectors and matrices, we need efficient computational tools. This chapter introduces NumPy (Numerical Python), the foundational package for numerical computation in Python.

NumPy provides a powerful object called the N-dimensional array (ndarray), which serves as the primary way to represent vectors, matrices, and higher-dimensional data structures. Its optimized routines make numerical operations significantly faster than using standard Python lists, which is essential when working with large datasets.

Throughout this chapter, you will become familiar with:

• The core NumPy ndarray object.
• Methods for creating NumPy arrays (from lists, using functions like zeros, ones, arange).
• Techniques for accessing and modifying array data through indexing and slicing.
• Performing basic element-wise arithmetic operations on arrays.
• Understanding key array attributes (like shape, size, dtype) and how to change an array's shape.

We will reinforce these concepts with practical examples, giving you hands-on experience in creating and manipulating NumPy arrays. This forms the practical basis for implementing the linear algebra operations discussed in later chapters.