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.
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.
Brandon has a BSc in Mathematics and is a master’s candidate for Predictive Analytics at Northwestern University. He learned R from scratch and is here to make sure you don’t have to.
Section Introduction Transcripts
Section Introduction Transcripts
Course Overview Hi, everyone. My name is Brandon Strain, and I'm a graduate student at Northwestern University. Welcome to my course. Applying linear algebra with our linear algebra is foundational to advance topics such as predictive modeling, engineering, computer science and machine learning. In this course, you learn how to understand the underlying mathematics behind some of the tools that you use every day and some of the emerging technologies of tomorrow. Some of the major topics that we will cover include matrices and their usefulness. You'll learn why matrices are awesome and why linear algebra is better than the algebra that you learned in school. You'll learn about solving system of linear Equations were surrounded by systems of linear equations, but none of them are perfect. You learn how to deal with in perfect systems and what to do when there isn't an obvious solution. In noisy data, you learn how to calculate any item in a pattern of numbers, such as the one doing a number in the Fibonacci sequence on pen and paper. This would take decades, but are you can do it in seconds. Dylan had a decomposed matrices to compress data or extract insights in today's world were drowning in data on how to capture this data and matrices and how to extract one of the mathematically most important and significant pieces of a data set on how to reconstruct important details from a decomposed matrix. By the end of this course, you'll know how to construct an ordinary least squares regression model, compress images by hand, using are and how to do elementary matrix operations to get you started in linear algebra before beginning this course, you should be familiar with basic math and basic our programming to include indexing data frames and storing are objects. I hope you'll join me on this journey to learn linear algebra with the applying linear algebra with our course at Pluralsight.