An excellently simple explanation of concepts of linear algebra and PCA. Applause for lector. I really liked this course and found it very useful for those newbies in machine learning like myself. I recommend this course to all my friends and others interested in.

Most challenging of all three courses but rewarding as well. Requires you have refreshed complex topics of Linear Algebra ( Khan academy and other you tube material are good starting point) . Looking forward to go even deeper in to this. Thanks Imperial !!!

I liked the course quite a bit. I found it quite challenging (I had never seen any PCA) but it always kept me very interested. I had to use several sources to read a little more about PCA and to complete the last exercises, the forum is very helpful.

Very good course, although the programming assignments are challenging and some background knowlege in linear algebra and vector calculus required. You will need to do some independent research at times. But the instructions are clear and concise.

This course is of excellent quality. The teachers captured the knowledge perfectly in the MOOC. Although if you do not have knowledge in Python, it will be very difficult to successfully complete the course. Thank you Professor and Staff Coursera

Excellent course - challenging yet rewarding with good feedback from the teaching staff.

The video and the transparent white board are also great - look forward to seeing more MOOCs from Imperial as well as the release of the upcoming book

Better than the previous two courses in the spec. by one aspect: additional helpful materials are clearly pointed-out. Thanks Marc Peter Deisenroth for your effort. The book of Marc Peter Deisenroth is also recommended. Great course.

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

It is not an easy course I needed to see the videos more than 1 time to understand, and taking the 2 courses before is significant to cope with this course. I thank instructor Marc Peter Deisenroth for teaching this course.

This one is harder, I took longer time to figure out the assignments. Some of the concept that appeared in the assignments were not included in the lectures. I do hope that the assignments could have clearer instructions.

Very nice course. It will be great to have a course on Statistics for Machine learning covering advanced concepts in probability theory. Thank you for offering such a great course. I have learnt a lot and enjoyed fully.

Very good 3 courses for those of us who are beginners in Machine Learning and IA! However I miss a whole course, perhaps the first one of then four, teaching us what we need to know about python, numpy and plotting.

A great course. Learnt a lot, a lot of Linear Algebra, Projections/ Geometry/ all of these Mathematical ideas would help greatly in understanding of Machine Learning concepts and applying them to real world data!!..

It was a very nice experience with this course. I learnt a lot of Python Coding. The coding exercise was really good. It was tough for me to code in Python. But I took time for it. thanks to the faculty members.

Really nice course and kudos to the instructor. Week 4 was a bit challenging, but still he made it quite easy for us to understand. Very happy to have gone through this course and completed the specialisation.

I am not sure why the rating is so low for this course.

Personally, I found this course really insightful as the instructor explains what the different statistical measurements mean, and why are they useful.

I will present my self with some amazing songs!!

Excellent staircase to the heaven for learning PCA.

Breaking the habit of struggling with hardcore bookish mathematics.

Loose yourself in this adventure!!

Well explained, some issues with assignments but some of them are to not just type and think a little.

May be one is a real mistake... hard time with it, but lot of learning too.

Challenging, but doable. Has some bugs in coding assignments, but clearing them out makes you understand things better. Get ready to spend extra time understanding the concepts.

Very challenging course, requires intermediate knowledge of Python and the numpy library. PCA week 4 lab was truly a mind-blowing experience, taking over 5 hours to complete.

This course is well worth the time. I have a better understanding of one of the most foundational and biologically plausible machine learning algorithms used today! Love it.

Practices and quiz are designed well while I will suggest to put more hints on programming parts, e.g., PCA. Overall, this series of course are pretty useful for beginner.

This is the best course of the specialization, its very hard but it lets you to understand very important concepts of what means dimensionality reduccion.

Great Job!!!!

This course demystifies the Principal Components Analysis through practical implementation. It gives me solid foundations for learning further data science techniques.

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.