I had big fun of PCA

Great course. Assignments are tough and challenging.

The hardest one but still useful, very informative neat concepts

excellent course by imperial

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

Lectures and programming assignments were selected nicely to teach the math of PCA

Very Good Course

Truly hardcore course if your are a noob in reduced order modelling. Very challenging

A little bit difficult but helpful, thank you!

Great, challenging course. The instructor will expect much of you as the material is not spoon fed. At times this is frustrating but yet that's the best way to build your own intuition. This is a *hard* course and I imagine most of machine learning is like this. Fun, rewarding, and challenging. You'll flex your math and programming muscles.

Excellent course and extremely difficult one to grasp at one go. Regards Arijit Bose

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.

This course is also so helpful, and the lecturer is so predominant on what he taught.

the course was very much intuitive and helpful to grasp the knowledge of PCA

This was a difficult course but still very informative.

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

This is an excellent course first covers statistics, looks back to inner products and projections, thereafter it connects all of that and introduces PCA. The knowledge that you've gathered throughout the first two courses gets applied here. Granted, it's more abstract and challenging than the others, I wouldn't give a worse rating just because of that. You'll need to dive into certain topics on your own and if you strengthen your coding skills for the programming exercises. Nevertheless, it's just as highly rewarding as the first two.

Good, short, overview of PCA

Great capstone for the three-class Mathematics for Machine Learning series. Assignments were way harder and programming debugging skills had to be appropiate in order to finish the class.

Many errors of homework

great material but explanation are a little bit messy

Programming assignments are a little difficult. Background knowledge of Python is recommended for this course.

I would have liked to be introduced to the topic on a higher level first - and then, step by step, an introduction of the math to solve specific problems in the progress. That would be a perfect approach, especially for data scientists who just want to understand the underlying math for such a widely used technique.

This course is way harder than the first two. The maths itself is more difficult. The Python parts are a lot more challenging because they require a good understanding of the way Numpy handles vectors and matrices. But the end result is good and it is worthwhile!

Good content, just need to fix the assignments' platform.