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Learner Reviews & Feedback for Mathematics for Machine Learning: PCA by Imperial College London

4.0
stars
2,276 ratings
570 reviews

About the Course

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms....

Top reviews

JS
Jul 16, 2018

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

NS
Jun 18, 2020

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

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476 - 500 of 566 Reviews for Mathematics for Machine Learning: PCA

By Nouran G

Oct 11, 2018

Course is inconsiderate to new learners in that new concepts were very sloppily introduced. Like the first two courses of the specialization, this course is shallow, shouldn't be anyone's introduction to the subject and is a refresher at best. Unlike the other two courses, it assumes python knowledge, doesn't explain relevant syntax in the assignments; which made me take a lot of long unnecessary detours to get the python implementation right.

By Marvin P

Apr 24, 2018

After the other two awesome courses of the specialization this one stays far behind my expectations. Weakest course of the specialization. Instructor is obviously knowledgeable but does not provide much intuition. Programming assignments are really difficult and at many points frustrating. 2 more weeks and therefore comprehensive instructions would be desirable. Couldn't appreciate that course as much as I wanted to.

By Michalis D

Jul 22, 2019

After having done the first two parts of the specialization, I am afraid this one didn't stand up to the high quality bar the previous two had set. The programming assignments are unnecessarily long and complex and the overall material is not as engaging, connected and concise. I might give it a good rating as a standalone but now I can't avoid comparing it to the other two parts of the specialization.

By Colin H

Oct 2, 2020

Course material good but programming exercises are poorly designed and cause a lot of problems - even when you have understood the material very well. So unfortunately part of the assessment is your ability to sort out the problems from a poorly designed exercise rather than reinforce what you have been learning.

Fix the programming exercises and the course could be very good.

By Daniel A

May 9, 2020

Compared to the first modules in this series, the instructor explains almost none of the intuitions behind the maths and will skip over large essential pieces required to complete assignments and quizzes. It assumes a wide knowledge of programming and broader maths that was handled significantly better in the earlier courses.

By Xiaoxiao L

Jan 4, 2021

This is the least effective course among the three courses in this specialization. The reading materials have no context. People who have not been around those math symbols have no idea what the reading materials mean. They are not intuitive at all. The design of the assignments are poor as well.

By Daniel U

Sep 27, 2018

Programming assignments seemed to be written from a completely different direction, and instructions are vague and misleading. (The math assignments were not so bad.) There was no staff or mrntor engagement in the forums during the period of the course.

By amit s

Feb 8, 2019

Unlike the prior courses in the series, topics not clearly explained and brought too sudden. Also none of calculations shown completely, instructor just wrote results in the end. Due to all these reason I was not able to finish the course.

By Kevin L

Sep 11, 2018

The course assignments could be improved dramatically, though the course itself has very good content if you want to have a taste of how linear algebra (predominantly) can be implemented to solve machine learning problems.

By shashank s

Feb 17, 2020

First two courses in this series are great but not this one. Lectures and exercises are not related. I do not feel like I have totally understood PCA. Was able to complete the final assignment thanks to the internet.

By Bohdan K V

Aug 13, 2020

The course is awful, it's nothing compare to previous 2 courses. It has a lot of errors in assignments objectives and quizzes! The explanation is complete crap! I'm wondering how was it even allowed on Coursera?!

By Ivo R

Nov 16, 2019

The theory is well explained and the level of complexity is very similar to a University course, but the assignment environment is buggy and the assignments are poorly designed and very frustrating.

By raghu c b

Apr 4, 2020

Needs to demo a little bit of code owing to the complexity of the course content.Lectures gives just a high level understanding only. Assignments are taking far more complicated than expected.

By Paulo H S G

Nov 27, 2020

Even though the videos and quizzes are well produced and informative, the assignments are so poorly designed that they can only bring about some frustration with the learning process.

By Anjali s

Aug 23, 2020

Faced a lot of problems in exercises. Don't feel that i have completely understood the concepts. This course can be made more learner friendly with better explanations.

By Vignesh N M

Sep 12, 2018

Explaination of many things are skipped, assumption was made by the instructor that lot of things were already known by the learner. It could have been much better.

By Christian D W C A B

Sep 29, 2020

Very enlightening but the course assignments are full of bugs and make it really hard to work with. The first two courses of the specialization were way better.

By Maksim S

Mar 25, 2020

The difficulty of the course is inadequate and the pace is not balanced. Requires a lot of search for additional resources to understand materials. I cancelled.

By Kovendhan V

Jul 11, 2020

After first two amazing courses in this specialisation, third course was a huge let down. One skill I learnt from this last course is patience.

By Martin H

Dec 8, 2019

Lack of examples to clarify abstract concepts. Big contrast in quality compared to the other courses in this specialization.

By Dipto H

Aug 7, 2020

Poor explanation by the instructor. Previous ones were very helpful. I didn't understand many topics well

By Lavanith T

Aug 21, 2020

Everything is okay but there is a huge drawback with the programming explanation part.

By Xiao L

Jun 3, 2019

very wired assignment, a lot of error in template code. The concept is not clear.

By Sai M B

Aug 3, 2020

The lectures were not clear. I had to use other sources to understand lectures.

By Pawan K S

Jun 20, 2020

This course was the hardest I encountered in this specialisation.