Classbaze

Disclosure: when you buy through links on our site, we may earn an affiliate commission.

Mathematics for Machine Learning: Multivariate Calculus

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginnin...
4.7
4.7/5
(4,767 reviews)
92,799 students
Created by

8.9

Classbaze Grade®

N/A

Freshness

8.7

Popularity

8.7

Material

Platform: Coursera
Video: 3h 20m
Language: English

Best Math and Logic classes:

Skillshare_logo_2020

Classbaze Rating

Classbaze Grade®

8.9 / 10

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

Freshness

Course content can become outdated quite quickly.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.

Popularity

8.7 / 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.

Material

8.7 / 10
Video Score: 8.1 / 10
The course includes 3h 20m 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.
The average video length is 6 hours 39 minutes of 52 Math and Logic courses on Coursera.
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.
9 tests or quizzes.

In this page

About the course

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.

What can you learn from this course?

What you need to start the course?

There is no prerequisite, anyone can begin this course.. This course is also great for beginners without any Math and Logic knowledge.

Who is this course is made for?

This course is suitable for beginners.

Are there coupons or discounts for Mathematics for Machine Learning: Multivariate Calculus ? What is the current price?

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.

Will I be refunded if I'm not satisfied with the Mathematics for Machine Learning: Multivariate Calculus course?

Coursera offers a 7-day free trial for subscribers.

Are there any financial aid 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 will teach this course? Can I trust Samuel J. Cooper?

Samuel J. Cooper has created 2 courses that got 478 reviews which are generally positive. Samuel J. Cooper has taught 266,377 students and received a 4.73 average review out of 478 reviews. Depending on the information available, we think that Samuel J. Cooper is an instructor that you can trust.
Dyson School of Design Engineering
Imperial College London
Dr Sam Cooper is an Associate Professor in energy science and materials design in the Dyson School of Design Engineering at Imperial College London. His PhD was on the characterisation and optimisation of battery and fuel cell electrodes through 3D imaging, simulation and machine learning. Sam is the leader of the TLDR (Tools for Learning, Design and Research) group who have a particular interest in the application of generative adversarial networks to design tasks.

8.9

Classbaze Grade®

N/A

Freshness

8.7

Popularity

8.7

Material

Platform: Coursera
Video: 3h 20m
Language: English

Classbaze recommendations for you