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

4.0
stars
2,198 ratings
548 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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126 - 150 of 543 Reviews for Mathematics for Machine Learning: PCA

By XL T

Apr 3, 2020

It is a bit difficult and jumpy. You will need some hard work to fill in the missing links of knowledge which not explicite on the lectrue. Overall, great experience.

By Fabrizio B

Oct 31, 2020

Definitely the most challenging of the course making up this specialization. Finishing it with full scores is proportionally far more satisfying!!! Well done Marc!

By S J

May 3, 2020

Your Teaching and Video quality is par excellence.....Thanks a lot for such amazing stuff...I am looking forward to joining more courses in the same line

By Christine D

Apr 14, 2018

I found this course really excellent. Very clear explanations with very hepful illustrations.

I was looking for course on PCA, thank you for this one

By Ananta M

Apr 20, 2020

Although the course was little out there and the instructor was trying his best to articulate a difficult topic, the overall experience is great.

By Prime S

Jun 24, 2018

Nicely explained. Could be further improved by adding some noted or sources of derivation of some expressions, like references to matrix calculus

By xiaoou w

Nov 21, 2020

great content however the programming part is too challenging for people without propre guidance in the subject. the videos aren't of much help.

By J A M

Mar 21, 2019

Solid conceptual explanations of PCA make this course stand out. The thorough review of this content is a must for any serious data researcher.

By Sateesh K

Sep 24, 2020

This course should be part of "gems of coursera". Excellent specialization, thoroughly enjoyed it. For me the 3rd course on PCA was the best.

By Moez B

Nov 24, 2019

Excellent course. The fourth week material is the hardest for folks not comfortable with linear algebra and vectorization in numpy and scipy.

By Hasan A

Dec 30, 2018

What a great opportunity this course offers to learn from the best in this simplified manner. Thank you Coursera and Imperial College London!

By Duy P

Sep 24, 2020

Excellent explanation from the professor!! Besides he is the author of the book Mathematics for Machine Learning. You should check it out.

By Alexander H

Jul 30, 2018

Highly informative course! Loved the depth of the material. Found this course content highly useful in my current project based on PCA.

By Prabal G

Oct 21, 2020

great course for mathematics and machine learning...A big thanks to my faculty to guide like a god in this applied mathematics course

By Jason N

Feb 20, 2020

A lot of reading beyond the video lectures was required for me and some explanations could be more clear. Overall, a great course.

By Rishabh P

Jun 17, 2020

Well-detailed course and straight to the point. I enjoyed the course even though the programming assignments can be challenging

By UMAR T

Mar 10, 2020

Excellent course it helps you understanding about linear algebra programming into real world examples by programming in python.

By Josef N

May 14, 2020

It would be great if the course is extended to 8 weeks, with the current week 4 spanning at least 3 weeks. Otherwise great.

By Dora J

Feb 3, 2019

Great course including many useful refreshers on foundational concepts like inner products, projections, Lagrangian etc.

By Trung T V

Sep 18, 2019

This course is very helpful for me to understand Math for ML. Thank you Professors at Imperial College London so much!

By Mukund M

May 24, 2020

Professor Deisenroth is amazing. Very tough course but appreciated all the derivations and explanations of concepts.

By David H

Mar 21, 2019

It was challenging but worth it to enhance the mathematic skills for machine learning. Thanks for the awesome course.

By Lee F

Sep 28, 2018

This was the toughest of the three modules. It gave me a strong foundation to continue pusrsuing machine learning.

By Nileshkumar R P

May 6, 2020

This course was tough but awesome. Lots of things i learnt from this course. Great course indeed and worth doing.

By Nishek S

Jul 30, 2020

The PCA part Was a bit tricky barely handle the concepts.

thank you imperial team for such interactive course