Mathematics for Machine Learning: Linear Algebra
Course Features
Duration
19 hours
Delivery Method
Online
Available on
Limited Access
Accessibility
Desktop, Laptop
Language
English
Subtitles
English
Level
Beginner
Teaching Type
Self Paced
Video Content
19 hours
Course Description
Course Overview
International Faculty
Post Course Interactions
Instructor-Moderated Discussions
Skills You Will Gain
What You Will Learn
Eigenvalues And Eigenvectors
Transformation Matrix
How to rotate images of faces
How the Pagerank algorithm works
Course Content
Module 1: Introduction to Linear Algebra and to Mathematics for Machine Learning
1. In this first module we look at how linear algebra is relevant to machine learning and data science. Then we'll wind up the module with an initial introduction to vectors. Throughout, we're focussing on developing your mathematical intuition, not of crunching through algebra or doing long pen-and-paper examples. For many of these operations, there are callable functions in Python that can do the adding up - the point is to appreciate what they do and how they work so that, when things go wrong or there are special cases, you can understand why and what to do.
Course Instructors
David Dye
Professor of Metallurgy
Course Reviews
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