5 Reasons to Learn Linear Algebra for Machine Learning
Last Updated on August 9, 2019
Why Learn Linear Algebra for Machine Learning?
Linear algebra is a field of mathematics that could be called the mathematics of data.
It is undeniably a pillar of the field of machine learning, and many recommend it as a prerequisite subject to study prior to getting started in machine learning. This is misleading advice, as linear algebra makes more sense to a practitioner once they have a context of the applied machine learning process in which to interpret it.
In this post, you will discover why machine learning practitioners should study linear algebra to improve their skills and capabilities as practitioners.
After reading this post, you will know:
- Not everyone should learn linear algebra, that it depends where you are in your process of learning machine learning.
- 5 Reasons why a deeper understanding of linear algebra is required for intermediate machine learning practitioners.
- Where to get started once you are motivated to begin your journey into the field of linear algebra.
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.