10 Examples of Linear Algebra in Machine Learning

Last Updated on August 9, 2019

Linear algebra is a sub-field of mathematics concerned with vectors, matrices, and linear transforms.

It is a key foundation to the field of machine learning, from notations used to describe the operation of algorithms to the implementation of algorithms in code.

Although linear algebra is integral to the field of machine learning, the tight relationship is often left unexplained or explained using abstract concepts such as vector spaces or specific matrix operations.

In this post, you will discover 10 common examples of machine learning that you may be familiar with that use, require and are really best understood using linear algebra.

After reading this post, you will know:

• The use of linear algebra structures when working with data, such as tabular datasets and images.
• Linear algebra concepts when working with data preparation, such as one hot encoding and dimensionality reduction.
• The ingrained use of linear algebra notation and methods in sub-fields such as deep learning, natural language processing, and recommender systems.

Kick-start your project with my new book Linear Algebra for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.