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

4.0
stars
3,108 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 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.

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.

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301 - 325 of 776 Reviews for Mathematics for Machine Learning: PCA

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 Xi 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.

By Jyothula S K

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May 18, 2020

Very Good Course to Learn about PCA

By Thierry P

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Apr 21, 2022

good understanding of pca insight

By Carlos S

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Jun 12, 2018

What you need to understand PCA!!!

By 祈璃

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Jul 9, 2021

This module is quite challenging!

By Dina B

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Aug 8, 2020

Nice course - informative and fun

By saketh b

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Aug 10, 2020

The instructor did a great job!

By Sukrut B

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Oct 19, 2020

Try to make it little bit easy

By Javas A B Y P

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Mar 28, 2021

Alhamdulillah, this is great!

By Israel d S R d A

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Jun 6, 2020

Great course very recommended

By Muhammad T

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Mar 2, 2021

haha good course i completed

By Jonah L

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Dec 7, 2020

It's hard but it's worth it!