Amazing Course!!!!

Great instructor!

Tough, but great!

AWESOME!!!!!!!!!!

Truly exceptional

Very Good Course

Great course !

Great course!

Nice Course.

Great course

Best course

Many thanks

good course

fantastic

RRhis cl

awesome

Great !

good

good

good

The overall experience was very good. I have enjoyed all the math in videos and PCA derivation throughout the course. The course a bit harder than the previous ones in the specialization. However after some effort one can understand the points that is not taught thoroughly. Only downside of the course is the programming environment. I have attended different courses that are also using Jupyter notebooks on Coursera and they were flawless. Here we have, some cells do run forever, a grader behaving inconsistently and one week that has some steps completely against the general software engineering principles. By the way discussion forums are so helpful and make me understand some math concepts on the way. I recommend the course to people who want to improve their understanding of math before deep diving machine learning courses.

This is the final course in the Specialization, that focuses on Principal component Analysis.This course is a bit hard compared to the other two courses in specialization. This builds on the topics explained in the other two courses.The Instructor tries to squeeze the concepts in the limited time.Not all materials are completely explained in the video, however, students can refer to other materials available in the web/ Refer the course forums and get the concepts and use them to solve the Quizzes. Some times the Assignments and quizzes are frustrating , however they do a good job of reinforcing the ideas taught in the video. Totally this is a good time spent .

it is good, challenging course. i've learned a lot, but feel that i came away with quite patchy knowledge. This course is a big step up in complexity and delivery form the previous two courses. perhaps my expectations were not right to start with - one cannot learn this level of complexity so quickly. Admittedly there are many gaps between the lectures and course materials and what is asked in programming assignments. i ended up reading a lot online to fill in the gaps (i've learned a lot of python during the course, which is great!).nevertheless, after this course i feel equipped to continue with machine learning.

I think this course is slightly underrated at the moment. The topic is not an easy one and I thought the teacher did a great job of explaining it as clearly as possible using an appropriate amount of mathematical derivation.

I really thought the last week of the course was great, especially considering that everything we had seen so far in the specialization was used to develop the PCA algorithm. It's quite amazing how topics such as eigenvectors, projections and optimization all come together here.

I think the notebooks were quite challenging compared to the previous two courses with is definitely a plus!

Instructor gives the very dry but useful essence of the "philosophical" concepts of dot and generalized inner product, etc., - personally, liked that. Unfortunately, the offered problems are so far away from the delivered videos but the web search helps on getting the hints. This course makes you think - I learned a lot just by asking myself "what do they mean under this statement?", what they want in this task? Though I will appreciate if providers elaborate the material further and so instead of googling we spend our time watching - a single point access.