Challenging course, a lot harder than the two previous in the specialisation. Having said that, I really enjoyed it for the insights that it gave and for actually making me learn some Python as well. With this course you need to go search and fin the necessary functions and usage to complete the assignments. The best course in the series I believe.

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.

Fantastic teacher !! He succeeds in finding the right balance between theory and concrete examples. All the concepts presented over the 4 weeks smoothly merge at the end of the course to give a good global picture of the PCA algorithm and its applications. As a sidenote, the Jupyter notebooks contain mistakes or can be quite confusing.

A great course, worth the money. It was hard, as it should be. The explanations are concise, and the assignments take much more to complete, at times leaving us scratching the head. Anyway, I'm so glad to have completed, it has provided me such great insight about how mathematics powers the machine learning algorithms we use everyday.

This is by far the best course I have taken. The Instructor is exceptional in setting the stage to understand the complex topic by letting us know the motivation of every concept, making us understand the fundamentals right, deep diving into the core of the topic and them nicely summarizing the topic along with the applications.

The course is fun because it applying math such as linear algebra to pca. Projection matrix is interesting to see it and do it in coding. The last programming assignment pca part need slightly more thought than previous two courses but is highly do-able, enjoyable and feel good after getting 10/10 after figure it out.

This course is amazing. But you if you guys maybe in the future to make some small example. I really dont get the concept when there is no example. I mean the example with a number in it or maybe i said the direct implementation. But all is great. Thanks you for teaching me this. I hope you guys well. thanks

This course is at a higher level than the first two in the specialisation, and the instructor focusses on the mathematics of matrices, while the assessments are programming. There are easier courses for just PCA (which I thought helped me). Looks like most learners find a way through, and its worth it.

It was a great course. Challenging at some points since I'm new in Python but it was worth the effort and I really learn a lot and now I comprehend the maths behind PCA algorithm. The point in which the relationship between eigenvalues of the covariance matrix is used in the PCA algorithm was amazing.

Even though I had lots of problems with the last coding exercise, I still learned a lot from this course. I loved how the instructor went from the basics of statistical representation and started using all of these tools in order to show us how the PCA algorithm works and why is it effective.

Course 3 was quite challenging when compared to 1 and 2.

But, the instructor have explained the concept very well, the coding assignments were bit confusing and time killing.

Got to learn some important ML mathematics and the concept of projection, inner product and PCA were amazing.

Thank You

Learning Mathematics in this way and in efficient manner from basics and very clearly is really nice. I am very thankful to this course , teachers, Imperial College London as well as team of Coursera for providing such a great platform to learn all these skills and enhance our knowledge.

This was indeed a very challenging course. It was also very rewarding, and I felt that the instruction was great and relevant to the assigned tasks. The first two courses in the specialization were very high quality, and in my opinion this one lives up to the expectations that they set.

This is a good course coming with a very good book which you can use to reference later on even if you don't fully understand what or how PCA derives.

The exercise & lectures were interesting and guiding you enough to pass all tests. Take note and reference the book are keys to succeed.

Very interesting and challenging subject: PSA, this MOOC together with the other 2 Mathematics for Machine Learning are one of the most useful I have ever made, actually they helped a lot in my other Machine learning and Deep learning studies! I highly recommend this fascinating MOOC

Perfect course. It takes up more time and effort than the other two courses in the specialization. But what you learn by the end of it is totally worth the effort. Note that this is an Intermediate course compared to the other two which are beginner. So the extra rigor is expected.

The introduction to PCA and steepest descent algorithms which might be a century old but still act the fundamentals of many state of art equations. So, you will learn the basics that how they function, and the real mathematics you need to know for ML using this course.

A really great course with mant well explained interesting concepts .. I personally liked the Python coding assignments in this course because it made me learn so much Python applied to very interesting problems .. Thanks for imperial college for such amazing work !!

Its a very informational and interesting course. I understood a lot about PCA in this amazing course.

It was a good addition to the previous two courses of the certification. I would like to get similar courses in statistics and probability useful in Machine learning.

Great course! It is a bit more challenging than the other courses in the specialization. It is great that this course is built based on two other previous courses. The lectures are great, the quizzes and programming assignments are complex enough to be interesting.

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