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.
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.
por 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
por 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.
por 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.
por Moez B•
Nov 25, 2019
Excellent course. The fourth week material is the hardest for folks not comfortable with linear algebra and vectorization in numpy and scipy.
por Hasan A•
Dec 31, 2018
What a great opportunity this course offers to learn from the best in this simplified manner. Thank you Coursera and Imperial College London!
por 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.
por Alexander H•
Jul 31, 2018
Highly informative course! Loved the depth of the material. Found this course content highly useful in my current project based on PCA.
por 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.
por 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
por UMAR T•
Mar 10, 2020
Excellent course it helps you understanding about linear algebra programming into real world examples by programming in python.
por 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.
por Dora J•
Feb 04, 2019
Great course including many useful refreshers on foundational concepts like inner products, projections, Lagrangian etc.
por Trung T V•
Sep 19, 2019
This course is very helpful for me to understand Math for ML. Thank you Professors at Imperial College London so much!
por Mukund M•
May 24, 2020
Professor Deisenroth is amazing. Very tough course but appreciated all the derivations and explanations of concepts.
por David H•
Mar 21, 2019
It was challenging but worth it to enhance the mathematic skills for machine learning. Thanks for the awesome course.
por Lee F•
Sep 28, 2018
This was the toughest of the three modules. It gave me a strong foundation to continue pusrsuing machine learning.
por Nileshkumar R P•
May 06, 2020
This course was tough but awesome. Lots of things i learnt from this course. Great course indeed and worth doing.
por Nishek S•
Jul 30, 2020
The PCA part Was a bit tricky barely handle the concepts.
thank you imperial team for such interactive course
Aug 21, 2019
One of the most challenging course in my life - almost impossible without python and mathematics background.
por Pratama A A•
Aug 26, 2020
Need more Effort to grasp the materials explained_-" you need to be patience,the lecturer is really on top
por Nelson S S•
Jul 29, 2020
Excellent course ... Quite challenging, a little difficult but I have learned a lot ... Thank you ...
por sameen n•
Sep 06, 2019
Amazing course and provides basic introduction for the PCA. Need for programming help in this course.
por Brian H•
Feb 25, 2020
Great course. I appreciate the rigor and clear mathematical explanations provided by Dr. Deisenroth.
por Natalya T•
Feb 25, 2019
exellent course! nice python wokring enviroment and very good explanation at each topic. thank you!
por Aishik R•
Jan 18, 2020
Excellent and to-the-point explanations, useful assignments to make the concepts etched in memory