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Mathematics for Machine Learning: Linear Algebra

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices...
4.7
4.7/5
(9,894 reviews)
249,593 students
Created by

8.9

CourseMarks Score®

N/A

Freshness

8.6

Feedback

8.7

Content

Platform: Coursera
Price: $0
Video: 3h 46m
Language: English

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Detailed Analysis

CourseMarks Score®

8.9 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

8.6 / 10
We analyzed factors such as the rating (4.7/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

8.7 / 10
Video Score: 8.1 / 10
The course includes 3h 46m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 8.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.8 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

5 articles.
0 resource.
0 exercise.
8 tests or quizzes.

Table of contents

Description

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

Since we’re aiming at data-driven applications, we’ll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you’ll write code blocks and encounter Jupyter notebooks in Python, but don’t worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.

At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

You will learn

Requirements

There is no prerequisite, anyone can begin this course.. This course is also great for beginners without any Machine Learning knowledge.

This course is for

This course is suitable for beginners.

How much does the Mathematics for Machine Learning: Linear Algebra course cost? Is it worth it?

Access to most course materials is FREE in audit mode on Coursera. If you wish to earn a certificate and access graded assignments, you must purchase the certificate experience during or after your audit.

If the course does not offer the audit option, you can still take a free 7-day trial.

Does the Mathematics for Machine Learning: Linear Algebra course have a money back guarantee or refund policy?

Coursera offers a 7-day free trial for subscribers.

Are there any SCHOLARSHIPS for this course?

YES, you can get a scholarship or Financial Aid for Coursera courses. The first step is to fill out an application about your educational background, career goals, and financial circumstances. Learn more about financial aid on Coursera.

Who is the instructor? Is David Dye a SCAM or a TRUSTED instructor?

David Dye has created 2 courses that got 1,586 reviews which are generally positive. David Dye has taught 266377 students and received a 4.74 average review out of 1,586 reviews. Depending on the information available, David Dye is a TRUSTED instructor.
Department of Materials
Imperial College London
David Dye is a Professor of Metallurgy in the Department of Materials. He develops alloys for jet engines, nuclear and caloric materials so as to reduce fuel burn and avoid in-service failure. This involves crystallography (vectors and transformation matrices) and techniques like neutron and synchrotron X-ray diffraction and electron microscopy at the atomic scale. These give rise to ‘big data’ analysis problems associated simply with the amounts of data we can now collect. His Phd and undergraduate degrees were from Cambridge University in 1997 and 2000; he joined Imperial in 2003. He also teaches introductory mathematics – errors and data analysis, and has won student-led awards for innovation in teaching.

8.9

CourseMarks Score®

N/A

Freshness

8.6

Feedback

8.7

Content

Platform: Coursera
Price: $0
Video: 3h 46m
Language: English

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