# Linear Algebra for Machine Learning

Linear Algebra refresher for machine learning. Basics + Python implementation
Free tutorial
Rating: 3.3 out of 5 (9 ratings)
2,216 students
1hr 1min of on-demand video
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Understand the basic concepts of linear algebra
Implement the basic operations of matrices and vectors in python
Solve system of linear equations using python
Compute eigenvalues and eigenvectors of a matrix
Diagonalize a matrix using eigenvalues and eigenvectors

## Requirements

• Basics knowledge of linear algebra
• Basic knowledge of python

## Description

Linear Algebra is usually a prerequisite of machine learning. However, one doesn't need to know all the concepts in linear algebra.

In this course, I have compiled together all the important linear algebra concepts that are most frequently used in machine learning. This is the content I taught at Polytechnique Montreal as a refresher on linear algebra for machine learning. Understanding these concepts will help you navigate through an introductory course in machine learning.

This course is for you if

- You have some knowledge of linear algebra.

- You want to refresh some parts of linear algebra for machine learning.

This course is not for you if

- You want to learn linear algebra from scratch.

- You want to learn all important concepts in linear algebra.

- You don’t know anything about python.

Please note that I do not cover all the topics in linear algebra. I only cover the topics that are most frequently used in the machine learning textbooks. If you want to learn linear algebra from scratch or master all the concepts, this course is not for you.

In this course, we cover the following topics

- Vectors and Matrices

- Matrix operations

- Rank of a matrix

- Solving linear equations using matrix

- Change of basis

- Eigenvalues and Eigenvectors

- Diagonalization

- Norms

- Trace

## Who this course is for:

• Students who want to start their journey in machine learning and want to refresh the linear algebra topics needed for that.

## Instructor

Ex Googler and PhD Student at Polytechnique Montreal

I graduated from BITS Pilani Goa Campus in 2015 with B. E. (Hons.) Computer Science and M. Sc. (Hons.) Mathematics. After graduation, I worked with Google from 2015 to 2020 with operations research team. With the operations research team I worked as a developer of Google OR-Tools. I Left Google in 2020 to start my PhD under the supervision of Prof. Andrea Lodi and Prof. Guy Desaulniers in applied mathematics.

My research interests:

Discrete optimization

Column Generation

Machine Learning.