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Linear Algebra for Machine Learning
Linear Algebra refresher for machine learning. Basics + Python implementation
Goals
 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
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.
Course Structure
11 lectures • 01:01:29 total duration
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Your Course Instructor
Krunal Patel
Never stop learning.
Course content:
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