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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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576 - 600 of 758 Reviews for Mathematics for Machine Learning: PCA

By Ashish P

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Oct 21, 2020

Instructor has done lot of hard work. However, the course is little rigorous. If it is possible, I request the team to upload few more videos for this module. Nevertheless, thank you so much. I have still learned a lot from this course.

By helen l

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Apr 27, 2020

The content is decent but there are some bugs in the programming assignments. Particularly the last two programming assignments. The auto-grader for the second to the last assignment passes in some input that is not of the correct form.

By V K

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Jul 23, 2020

The course content was very good,but the assignments were harder as knowledge of python libraries was required. It would be very helpful if you change the assignments as I feel the course should rather be about math than python

By YUCHEN O

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May 1, 2023

The first three weeks courses are ok and can follow, lecture in last week are very difficult to understand as the teacher skipped some of the steps and straightaway gave the derivation and lots of bugs in assignments.

By Pierre

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Apr 10, 2020

Positive points: At the end of the module, you get a good understanding on how PCA works. It fulfill its objective.

Negative points: The assignements are poorly directed, the material is not always clearly explained.

By Alexander Z

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Sep 14, 2018

Good Course, but

Too less examples to do the quizes on the first run.

Programming assignments are not clearly stated, so you need unnecessary much time to succeed.

I liked the Linear Algebra & Multivariate Modul more!

By devansh v

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Apr 3, 2020

The course is Satisfactory.The content is Good,no doubt about it,but many topics(both mathematical and computational) were unknown and coding assignments of Jupyter notebooks of this course(PCA) are very Buggy

By Norah

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Aug 25, 2020

Kinda complicated but doable. The stuff do not monitor the discussion forums unfortunately. Without Susan's detailed & well informative replies I won't be able to complete the course. Big THANK YOU to Susan.

By Marina P

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Sep 6, 2019

The course is interesting, but some of the quizzes were not done very well. After the first 2 parts of this course, which were just amazing, this one seems kind of worse, although by itself its not that bad.

By Ahmed A

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Jun 19, 2022

They need to slow down while explaining concepts. The instructor assumes the viewers know each and every step. The other two Mathematics for Machine Learning courses were much better compared to this.

By Deleted A

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Jan 5, 2021

I was expecting this course to connect with the previous two but it turned out to be self contained. Jupyter notebooks contain inconsistent comments and assignment steps. Certain tasks were not clear.

By Rosie H

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Apr 1, 2021

The jump in difficulty for the final two modules was too hard going from the previous two courses in my opinion. also I would have liked more practical examples rather than being directed to reading.

By Chad K

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Jul 8, 2020

Difficult course. They need more formal tutorials to help with the gap between the videos and the tests and projects. I found it very helpful to buy the instructor's textbook and read along in it.

By Cécile L

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Apr 14, 2019

Amazing topic, great teachers and nice videos, but assignments can be slightly frustrating and some aspects (matrix calculus, derivatives, etc.) are really expedited... Still worth your time!!!

By Nicholas K

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Apr 27, 2018

It's a shame. There's lots of good material and I learned a lot. But a staggering amount of time was wasted figuring out gaps in the instructions - portions felt more like hazing than teaching.

By Paryant

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Dec 2, 2020

Instructor made a good attempt to cover these complex topic. However, these topics should be supported with more examples and also provide more intuitive examples as in previous 2 courses.

By Adrian C

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Sep 22, 2019

The derivatiion of the PCA in the last week can be broken into 2 weeks with different programming assignments to get a closer and more confident understanding of the PCA method.

By SINA M

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Feb 6, 2023

The course is good in general but some parts can be improved to be more detailed. The course seems to be abandoned despite having lots of bugs in the assignment sections

By Jean D D S

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Aug 31, 2019

I would ask the lecturer to go on more detail on the explanations and do (more) examples.

The lecturer tends to skip a few steps during calculations and demonstrations.

By David N

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May 5, 2021

Difficult course even having completed the two courses that precede it. Some concepts introduced here as assumed knowledge that were not covered in the prior courses.

By DD

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Jul 21, 2020

The week 2 code was more difficult than the other weeks. The forums are no longer attended by the professors. The access to materials from IC is great.

By Wang Z

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Jul 8, 2018

The knowledge introduced in this course is really helpful. However, the programming assignments are very time consuming and not necessarily relevent

By 詹閔翔

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Jan 17, 2021

Thank for the excellent course content but i think it would be nice if teacher could do more example or apply than just math formula introduction

By Iurii S

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Mar 26, 2018

Decent explanations of PCA idea, but assignments do not provide a clear feedback of what is wrong with the implementation util you get it right.

By bowman

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Aug 27, 2020

this is a great course except the assignment has quite a few bugs and the videos are too short and lack many topics, and the quiz are too short