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

4.0
stars
3,116 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

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.

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.

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626 - 650 of 781 Reviews for Mathematics for Machine Learning: PCA

By Thiha A

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Jan 18, 2022

I rarely use Discussion Forums. But for this course, I learnt all from the Discussion Forums. :(

By Chiu T W

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Mar 25, 2023

This programming assignments of this course have many bugs that you need to manually submit it.

By Alfian A H

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Mar 25, 2021

I think this course has many flaws. Some of the explanation from instructor isn't very clear.

By Saurabh M

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Oct 11, 2020

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

By Suprith R G

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Sep 16, 2019

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

By Vishesh K

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Mar 13, 2020

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

By Sagun S

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Mar 14, 2019

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

By Keng C C

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May 30, 2020

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

By Matan A

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Oct 20, 2019

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

By Yuxuan W

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Oct 5, 2018

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

By PS

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Mar 2, 2021

Too much material covered too quickly. Needs to be split into seperate modules.

By Sethu N O G

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Aug 16, 2020

faculty must improve his teaching techniques.

I found the course less interesting

By Rafael C

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Dec 7, 2019

definitely one of the most catastrophic courses I've ever taken on Coursera...

By Sherryl S S

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Mar 7, 2021

Not enough explanation, minimum instructions, hard projects, lots of errors.

By Meraldo A

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May 8, 2018

The course content was good; however, it was not well explained at times.

By connie

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Mar 21, 2020

I think content of first 2 weeks are disconnect with 3rd and 4th weeks

By Sasha

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Nov 6, 2019

Math for the sake of math. Too big jumps in calculations, too complex.

By Muhammad I U H

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Aug 22, 2023

The instructor method of teaching is too static or robotic in nature.

By k v k

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Nov 29, 2018

its a good course to learn mathematics essential for machine learning

By Rafael C

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Sep 24, 2019

The Classes didn't give the knowledge to solve the assignments.

By Shuyu Z

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Oct 18, 2019

The videos and instructions for the assignment are not clear.

By Andrew D

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Jul 21, 2024

Excellent course let down by bugs in Coursera gading system

By gaurav k

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Jul 3, 2019

More examples and visualization should be there to explain.

By Camilo V

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Sep 23, 2023

There are error for submitting the programming assigments

By Fakhrutdinov A

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Jan 21, 2025

lack of info for the programming assignments in course 3