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

4.0
stars
2,165 ratings
536 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 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.

NS

Jun 19, 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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176 - 200 of 531 Reviews for Mathematics for Machine Learning: PCA

By Isaac M M

Aug 09, 2020

A bit more difficult than previous ones but it is worth it

By Phani B R P

Jun 01, 2020

Very good course and extremely challenging, especially PCA

By Md A A M

Aug 24, 2020

Great Course. Everyone should take this course. Thanks.

By Harish S

Nov 24, 2019

This was a difficult course but still very informative.

By Oleg B

Jan 06, 2019

Excellent focus on important topics that lead up to PCA

By Prateek S

Jul 14, 2020

best course and important to study with concentration

By Lahiru D

Sep 16, 2019

Great course. Assignments are tough and challenging.

By Archana D

Mar 06, 2020

Brilliant work, references and formulas aided a lot

By Tichakunda

Jan 18, 2019

good course, rigorous proof and practical exercises

By Diego S

May 02, 2018

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

By CHIOU Y C

Feb 03, 2020

A good representation after preceding courses.

By Wang S

Oct 21, 2019

A little bit difficult but helpful, thank you!

By eder p g

Aug 09, 2020

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

By Murugesan M

Jan 15, 2020

Excellent! very intuitive learning approach!!

By Hritik K S

Jun 20, 2019

Maths is just like knowing myself very well!

By K A K

May 22, 2020

Learnt many new things I didn't know before

By Naggita K

Dec 19, 2018

Great course. Rich well explained material.

By Carlos E G G

Sep 28, 2020

Really difficult, but worth it in the end.

By Binu V P

Jun 08, 2020

best course I had ever done in coursera

By Jonathon K

Apr 13, 2020

Great course. Extremely smart lecturer.

By Xi C

Dec 31, 2018

Great course. Cover rigorous materials.

By Akshaya P K

Jan 25, 2019

This was a tough course. But worth it.

By THIRUPATHI T

May 24, 2020

Thank you for offering a nice course.

By Eli C

Jul 22, 2018

very challenging and rewarding course

By 任杰文

May 13, 2019

It's great, interesting and helpful.