Machine learning fundamentally relies on processing numerical data efficiently. While Python's built-in lists are versatile, they are not optimized for the kind of large-scale numerical operations common in data analysis and model building. This is where NumPy (Numerical Python) comes in. It provides the foundation for scientific computing in Python, introducing the powerful N-dimensional array object, or ndarray.

This chapter focuses on getting you proficient with NumPy. You will learn how to:

• Create NumPy arrays using various methods.
• Perform essential array manipulations, including indexing, slicing, and reshaping.
• Apply mathematical and logical operations efficiently across entire arrays using universal functions (ufuncs).
• Understand broadcasting, the mechanism NumPy uses to perform operations on arrays of compatible shapes.
• Utilize NumPy for basic linear algebra tasks and statistical calculations.
• Read data from and save array data to files.

Mastering NumPy is a necessary step for effectively using other data science libraries like Pandas and Scikit-learn, which are built upon it. By the end of this chapter, you'll be equipped to handle numerical data structures and operations central to machine learning workflows.