ML:Understanding Vectors and Matrices

Vectors and matrices are fundamental components in machine learning (ML). They are used to represent data, perform operations on data, and enable the implementation of algorithms that underlie many machine learning models.

In this we explore why vectors and matrices are essential, how they are used, where they are applied, when to use them, and provide small code examples to reinforce understanding.

1) Why Do We Need Vectors and Matrices in Machine Learning?

Vectors and matrices provide a structured and mathematical way to represent data and perform complex operations. The reasons we need them include:

Data Representation: Vectors and matrices are used to represent features, datasets, and transformations in machine learning. For example, a vector can represent a data point, and a matrix can represent a dataset or a transformation applied to the data.

Operations and Calculations: Many machine learning algorithms rely on linear algebra operations, such as dot products, matrix multiplication, and decompositions, to learn from data and make predictions.

Efficient Computation: Vectors and matrices allow for efficient computation, particularly when dealing with large datasets. Linear algebra libraries are optimized for operations on vectors and matrices…