Linear Algebra for Data Science and Machine Learning using R

Vectors, Matrices, Solving Linear Equations, Factorization, Eigenvectors, Least Squares, SVD

5.00 (2 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Fundamentals of Linear Algebra

Applications of Matrices, Vectors and operations on Matrices and Vectors with implementation in R

Solve Systems of Linear Equations and implementation in R

Matrix Factorization and implementation in R

Computation of Eigenvalues, Eigenvectors and Eigen Decomposition with their implementation in R

Solving Least Squares problems

Singular Value Decomposition with its implementation in R

Why take this course?

This course will help you in understanding of the Linear Algebra and math’s behind Data Science and Machine Learning. Linear Algebra is the fundamental part of Data Science and Machine Learning. This course consists of lessons on each topic of Linear Algebra + the code or implementation of the Linear Algebra concepts or topics.

There’re tons of topics in this course. To begin the course:

We have a discussion on what is Linear Algebra and Why we need Linear Algebra
Then we move on to Getting Started with R, where you will learn all about how to setup the R environment, so that it’s easy for you to have a hands-on experience.

Then we get to the essence of this course;

Vectors & Operations on Vectors
Matrices & Operations on Matrices
Determinant and Inverse
Solving Systems of Linear Equations
Norms & Basis Vectors
Linear Independence
Matrix Factorization
Orthogonality
Eigenvalues and Eigenvectors
Singular Value Decomposition (SVD)

Again, in each of these sections you will find R code demos and solved problems apart from the theoretical concepts of Linear Algebra.

You will also learn how to use the R's pracma, matrixcalc library which contains numerous functions for matrix computations and solving Linear Algebric problems.

So, let’s get started….