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

4.0
stars
2,274 ratings
569 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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426 - 450 of 565 Reviews for Mathematics for Machine Learning: PCA

By Adrian C

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 Jean D D S

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 Dominique D

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

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

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

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

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

By zohair a b

Jun 15, 2020

The First 2 courses of this specialization were very good. I really wish the instructor for this course went into a little more depth.

By Francisco F

Apr 26, 2020

Average quality with low regard for intuition. Content is often Wikipedia pages or references to own content (chapters of own book).

By D. H

Sep 30, 2020

The system is problematic, just take a look those complains in the forum. I also got very frustrated from the last assignment.

By NEHAL J

Apr 21, 2019

The course was highly challenging. I wish some of the explanations were detailed and the assignments had better instructions.

By DHRUV M

Jan 3, 2021

Course is very high level. many concepts were not understood especially in the last course. Assignments were many confusing.

By Ana P A

Apr 22, 2019

The professor of other two a way better. This one skips some steps in some explanation that makes the tasks hard to do

By Chuwei L

Apr 5, 2019

worse than previous courses of machine learning specialization. Really confused me when introduced the inner products.

By SYED H

Sep 17, 2020

The course needs to introduce more advanced technique and practical examples or create a new Advanced course on this

By Jyh1003040

Jul 9, 2018

Honestly this course is the one worthing attempting. However, last week's content is really messy and challenging.

By Hsueh-han W

Sep 20, 2019

many steps are not clear enough that I have to spend a lot of additional time to figure out the details.

By Saurabh M

Oct 11, 2020

This course is pretty hard. The most important pre-requisite for this course is persistence.

By Gurudu S R

Sep 16, 2019

Tutor is not clear and concise on the concepts. Need more examples for Week 2 and Week 3.

By Vishesh K

Mar 13, 2020

Good Content but isnt't explained well. if you are motivated by yourself then go for it.

By Sagun P S

Mar 14, 2019

Tough one if you are new to programming or doesn't have excellent understanding of Maths

By Keng C C

May 30, 2020

explanations are not clear, need to refer to lots of youtube to catch up with course.

By Matan A

Oct 20, 2019

The is a lot of gap from what the lecturer learn and what the assignments requires.

By Yuxuan W

Oct 5, 2018

Always spending much more time on coding than needed. Same result but no credit :(

By Sethu N O G

Aug 16, 2020

faculty must improve his teaching techniques.

I found the course less interesting