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

4.0
stars
3,045 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 758 Reviews for Mathematics for Machine Learning: PCA

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

By Kevin O

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Mar 27, 2021

Really interesting topic but not nearly enough detail.

By Amr F M R

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Sep 22, 2020

I think course material was not explained well at all.

By Timothy M

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Apr 22, 2021

The lectures and assignments did not synergize well.

By Aravind

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Sep 23, 2019

Need to improve the content and delivery of content.

By Mohammed A A

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Jul 19, 2020

the course is too shallow with difficult code exame

By Scoodood C

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Jul 28, 2018

Video lecture not as intuitive as previous courses.

By Michael B

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Nov 21, 2019

Programming assignments not well explained

By youssef s

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Jul 27, 2020

very poor explanation of things

By Murilo F S

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Jan 24, 2021

not good teacher :Z

By Salah E

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Aug 4, 2020

again too hard

By Alan

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Aug 4, 2020

Very disappointing compared to the other courses. Recommend a complete revision of the course materials. Quizzes often had nothing to do with the preceding video. I worked through a week two quiz using the extensive notes in the discussion forum and by searching the internet. The next lecture proceeded in the same vein: the instructor failed to cover the material in video leaving me to figure out what the material was and then figure out how to find that material on the internet or in reference books. At that point, it just was not worth the time to take the course.

By 周玮晨

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Jun 8, 2018

This course is far far far behind my expectations.The other two course in the specializition is fantastic. There is no visualization in this course, Instructor is always doing his algebra, concepts are poorly explained. I can't understand a lot of concepts in this course because of my poor math background.But why do i take ths course if i have a solid background in math? Programming assignments is not difficult but hard to complete because of vaguely clarification.Plenty of time wasted to find what should i do, its' really frustrating.