Linear Algebra for Machine Learning

Linear Algebra for Machine Learning, available at Free, has an average rating of 4.1, with 13 lectures, based on 25 reviews, and has 3413 subscribers.

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You will learn about 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 This course is ideal for individuals who are Students who want to start their journey in machine learning and want to refresh the linear algebra topics needed for that. It is particularly useful for Students who want to start their journey in machine learning and want to refresh the linear algebra topics needed for that.

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Summary

Title: Linear Algebra for Machine Learning

Price: Free

Average Rating: 4.1

Number of Lectures: 13

Number of Published Lectures: 13

Number of Curriculum Items: 13

Number of Published Curriculum Objects: 13

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Vectors and Matrices

Lecture 3: Matrix operations

Lecture 4: Rank of a matrix

Lecture 5: Solving system of linear equations using matrix

Lecture 6: Change of basis

Lecture 7: Eigenvalues and Eigenvectors

Lecture 8: Diagonalization

Lecture 9: Norms

Lecture 10: Trace

Lecture 11: Assignment

Lecture 12: Congratulations

Lecture 13: bonus goodies

Instructors

Krunal Patel
Ex Googler and PhD Student at Polytechnique Montreal

Rating Distribution

1 stars: 2 votes

2 stars: 1 votes

3 stars: 3 votes

4 stars: 6 votes

5 stars: 13 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!