You can focus on linear algebra for a month and start a burning desire to learn more that will last the rest of your life. You will find that learning this logical discipline well has benefits that carry over into other areas. You develop out your natural analytical skills and look at everyday matters in a more rational matter. You develop confidence that results from having accomplished something difficult. If all of this intrigues you, then read on.
Data science is a thing of beauty. Part of what makes it beautiful is that it is actually a wonderful blend of disciplines. Computer science, math, statistics, artificial intelligence, the scientific method, data visualization and analysis all have parts to play in this magical symphony. What “makes things go” at its most basic level is math. A large part of that math can be defined as linear algebra. A matrix is a data structure consisting of all of the same type of element. It is a rectangular array or table of numbers, symbols, or expressions, arranged in rows and columns, and these represent a mathematical object or a property of such an object. This object can be manipulated, and what it contains as well as how it transforms is informative and useful. Ultimately, most machine learning models can be expressed in matrix form. A dataset itself is often represented as a matrix. Linear algebra is therefore used in data preprocessing, data transformation, and model evaluation. Therefore if you have a fundamental understanding of linear algebra you can build an intuitive sense of why and how a data model works.
It is possible to gain a fundamental understanding of linear algebra even if you never considered yourself especially adept at math. The technology and information that exist today make it more accessible than ever before.
Resource One. Coursera offers a highly rated series of courses on the mathematics for machine learning. This course defines linear algebra and how it relates to vectors and matrices. It teaches you to manipulate vectors and matrices, as well as making eigenvalues and eigenvectors relatable. Most important, it tells you how to use these to solve…