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

4.0
stars
3,098 ratings

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

WS

Jul 6, 2021

Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.

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.

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676 - 700 of 773 Reviews for Mathematics for Machine Learning: PCA

By Xiaoxiao L

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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 Alois H

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Feb 18, 2021

This course has been a nightmare. Dense and obscure lectures, "challenging" assignments asking for things that haven't been properly taught in the lectures and often unclear instructions.

Yes, some useful concepts are taught but overall it's rather a waste of time.

By Daniel U

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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

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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

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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

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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

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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

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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

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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

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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 Yi S

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Jun 11, 2021

assignment and quiz are not well designed. the knowledge covered in lectures are not enough to complete assignments. The first two courses in this specialization is much better.

By Nidhi G

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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 Tushar G

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Feb 9, 2023

not a good course for beginners in machine learning, concepts need to be explained more clearly. Other courses in this specialization were way better than this one.

By vignesh n

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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 Maksim S

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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 Ghanem A

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Jul 20, 2021

Response to questions is very slow. Support to learners is not sufficient

Programming assignments are not explained well (some I believe have errors)

By Kovendhan V

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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

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Dec 8, 2019

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

By Jamiul H D

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Aug 7, 2020

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

By Lavanith T

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Aug 21, 2020

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

By Xiao L

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Jun 3, 2019

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

By Sai M B

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Aug 3, 2020

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

By Pawan K S

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Jun 20, 2020

This course was the hardest I encountered in this specialisation.

By Mohamed A H

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Aug 18, 2021

it was not clear alot of the time and it was really hard

By Kirill T

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Jul 26, 2020

Way worse than the previous courses. Lacks explanations