Summary  
This chapter explains how to use NumPy’s linear algebra functions to perform core array operations such as dot products, transposition, matrix inversion, singular value decomposition, and eigenvalue/vector computations.

General domain of usage  
Scientific computing

`NumPy` offers a plethora of **functions** for executing **linear algebra operations** on arrays, including **matrix multiplication**, **transposition**, **inversion**, and **decomposition**. Key functions include:
- **`dot()`**: Computes the **dot product** of two arrays;
- **`transpose()`**: **Transposes** an array;
- **`inv()`**: Computes the **inverse** of a matrix;
- **`linalg.svd()`**: Performs the **singular value decomposition** of a matrix;
- **`linalg.eig()`**: Determines the **eigenvalues** and **eigenvectors** of a matrix.

In this project, we will delve into the fundamentals of NumPy, exploring its core features and uncovering the reasons behind its significant impact on scientific computing. By understanding its architecture and capabilities, we will grasp why it is considered an essential tool for data scientists, researchers, and developers working in the realm of data analysis, machine learning, and beyond.

In this project, we will delve into the fundamentals of NumPy, exploring its core features and uncovering the reasons behind its significant impact on scientific computing.

Getting into NumPy Basics

Linear Algebra Operations

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