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

4.0
stars
3,144 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 7, 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 17, 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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301 - 325 of 786 Reviews for Mathematics for Machine Learning: PCA

By Diego S

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May 2, 2018

Difficult! But I did it :D And I learnt a lot...

By Ida B R A M M

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Mar 27, 2022

Very HARD but fundamentals are important, yes?

By Levi C

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Feb 3, 2020

A good representation after preceding courses.

By Wang S

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Oct 21, 2019

A little bit difficult but helpful, thank you!

By eder p g

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Aug 9, 2020

excellent!!!! it's very useful and practical.

By Murugesan M

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Jan 15, 2020

Excellent! very intuitive learning approach!!

By Mohd. F I S

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Jun 8, 2022

Good. But Programming exercise is not clear

By Hritik K S

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Jun 20, 2019

Maths is just like knowing myself very well!

By K A K

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May 22, 2020

Learnt many new things I didn't know before

By Naggita K

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Dec 19, 2018

Great course. Rich well explained material.

By Sivasankar S

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Aug 3, 2021

This course is very informative and useful

By Carlos E G G

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Sep 28, 2020

Really difficult, but worth it in the end.

By Zongrui H

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May 11, 2021

PCA assignment in week4 is a chanllenge!

By Binu V P

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Jun 8, 2020

best course I had ever done in coursera

By Jonathon K

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Apr 13, 2020

Great course. Extremely smart lecturer.

By Ronnie C

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Dec 31, 2018

Great course. Cover rigorous materials.

By Juan B J

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Apr 3, 2023

Very interesting, thank you very much.

By Akshaya P K

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Jan 25, 2019

This was a tough course. But worth it.

By Carlos A V P

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Jan 16, 2022

Excelente curso, muy claro y retador

By Wassana K

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Mar 22, 2021

Programming Assignment is so hard !!!

By THIRUPATHI T

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May 24, 2020

Thank you for offering a nice course.

By Eli C

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Jul 22, 2018

very challenging and rewarding course

By Indria A

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Mar 26, 2021

very very tiring but fun, thank you.

By Jeff D

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Nov 1, 2020

Thank you very much for this course.

By 任杰文

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May 13, 2019

It's great, interesting and helpful.