Applying Linear Algebra with R

by Brandon Strain

Take this course if you want to dive into the math behind regression, principal component analysis, and other machine learning topics. The course is in R and is mathematically intense, but the topics can be implemented in any programming language.

What you'll learn

Would you like to better understand the basics of linear algebra so that you can better understand the techniques used in regression and machine learning? In this course, Applying Linear Algebra with R, you will learn foundational knowledge to understand what is going on in predictive models, how to extract important information from large data sets, and the basics of linear regression in R. First, you will learn basic matrix arithmetic. Next, you will discover advanced matrix mathematics that will help build your foundation. Finally, you will explore how to put this math together into real world applications. When you are finished with this course, you will have the skills and knowledge of Linear Algebra in R needed to better implement basic machine learning techniques and springboard into more advanced topics like generalized linear models.

Table of contents

Course Overview
2mins
Working with Vectors and Matrices in R
7mins
Understanding Operations on Matrices
5mins
Getting Weird: Inverting, Transposing, and Row Equivalence
10mins
Solving Linear Equations
8mins
Understanding and Calculating Eigenvalues and Eigenvectors
8mins

About the author

Brandon has a BSc in Mathematics from Huston-Tillotson University and is a master’s candidate in Northwestern University's Predictive Analytics program, where he specializes in predictive modeling. He spent seven years in the U.S. Navy where he developed a pragmatic and engaging teaching style. Brandon's aversion to academic nonsense and penchant for student engagement earned him jobs training the Navies of Senegal, Cabo Verde, Gabon, and Nigeria on behalf of the United States. Brandon began lea... more

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