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I want to make this more of a guideline rather than a direct catch & read Review because of the nature of this course. But first, congratulations to all who have managed to pass this course. Now the big discussion. If you have taken the enrollment prior to the other courses under the specialization, then you have several decisions to make. First of all, this course requires HIGH PATIENCE & good HOMEWORK times. This course is also HIGH on programming. So, if you are not familiar with Numpy, then you have to put more PATIENCE than before. Thereby, if you are a newbie in Numpy & up for the challenge to learn the steps & then implement on the code, you should consider enrolling in this course. Those who lack in PATIENCE & code-correcting scenarios, should not enroll in this. I am not going to rate this course (although, without putting stars I cannot submit this writing). Why? This is a 5-star course if you judge the difficulty & advanced topics covered throughout. This is a 4-star course if you seem to find your linear algebra knowledge start to tumble sometimes & the coding assignments are up for the game with lack of clarity. This is a 3-star course because of the Instructor's approach to explaining the abstractness of the higher dimensions. If you go more abstract in already more abstract things, that is more like adding salt to the wound. This is a 2-star course if you all on a sudden realize that the entire knowledgebase around Linear Algebra is falling apart & (AND) the coding assignments are feeling like a living mystery, especially the instructions may sound more confusing. This course is not a 1-star & if anyone rates it a 1-star that is because he/she is a sore loser. Nothing goes without effort. The whole team definitely put effort to cover the complexity and balance in between. But they weren't quite successful. If you up for a challenge, you are welcome to get into it. If you are hesitant, have some ice-cream & try later. Thanks.

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 .

The lectures and readings are very good, but the programming assignments are buggy and frustrating. It's hard to suggest improvements, as I think some of this material is just quite hard to assess in a moocs format. You might get some value from the free textbook "Mathematics for Machine Learning" by Marc Deisenroth, who is also the instructor for this course. You can learn a lot from him, even if the assignments are less useful than hoped.

By the way, don't worry if you lack programming experience. There's not much in the way of actual Python programming, as numpy functions already exist for pretty much everything you need to do.

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!

Good overview of the derivation of PCA, including reviews of the math from previous courses. My only complaints are that the lectures and descriptions were very dense and notation-heavy, and that there wasn't sufficient explanation for why certain operations were important to the task at hand. A lot is left to the reader to infer from understanding of the underlying maths. There is also little mapping of PCA back to real-world machine learning practices, except the optional final lecture. All-in, the material was useful, but could have been made easier to understand/more relevant.

It's a lot harder compared to the earlier courses in the specialization. Video lessons focus more on the theory and lack the visualization and practice problems of the previous courses. Some of the programming assignments can still be polished by including the discussion in between the codeblocks like the assignments in the previous course. Assertion errors in the notebook do not always reflect possible assertion errors in the grader. The difficulty reminds me of doing my own research and debugging my codes during college and those are useful lessons in graduate school and life.

There is so much to learn in this course and knowledge of linear algebra and calculus is required. The instructor takes a more traditional mathematical teaching style, showing the formula first and then proving the formula a lot of times. I really loved the richness and thoroughness of the Python lab exercises. I saved these Python codes for future uses. The only improvement I can think of is that there could be additional lectures to quickly go over/ summarize the reading materials. Overall, this is an excellent course if you have the will power to stay till the end.

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.

There were more typographical errors in this course compared to the first 2. In general, it was more challenging, but not in a good way. Often times I had to resort to looking at the discussions in the forums, and I simply wouldn't have been able to finish it without doing so. Unlike in the first 2 courses where I could spend a long time doing the exercises, but I could figure it out from my own notes. There also seemed to be some problems with the autograders of the Jupyter notebooks that the staff may want to review. Still, I learned a lot. Thanks team!

This course was a very interesting end to the specialization. The first two courses teach you the tools necessary, while this course teaches practical (although highly theoretical) application of those tools. The reason for the non-five star rating is the python exercises take one on quite a journey, which often times goes through some dark and murky tunnels which are tough to escape. Also, there are few worked examples during lectures.

It was a real tour de force on the mathematics, and I had some hard time following the ideas of the instructor many times. However, the topic was completely covered in a very systematic way, which is excellent in my opinion. My only suggestion is to focus more on what really matters: do we really have to spend such a long time discussing about different metrics for an inner product if in the end we only use the euclidean metric?

An in-depth exploration of the PCA algorithm and the math behind it. Python programming exercises helped me to solidify the theory and derivations. Numpy is used extensively in the exercises and I liked the experience that I gained in applying it. I found that the connection between the exercises and theory were not always clearly drawn and needed to refer to the discussion to fill in the gaps.

Would give this course 5 stars if it was properly described so that expectation could match reality:

Give yourself plenty of time for this course - it will take quite a bit longer than described.

Make sure you are comfortable with Python and NumPy before you start (particularly the linear algebra functions).

It is very different (much less hand-holding) than the other courses in the specialization.

its a good course, some exercices are not for beginner programers. I think on the PCA chapter the projection matrix is described wrong, I used the formula from previous weeks and it worked. I think the relation between kmeans and pca is not explained only on a programming task is breafly discussed but its described out of nowhere so you have to read the code to understand whats going on.

This course brings together many of the concepts from the first two courses of the specialization. If you worked through them already, then this course is a must. There are some issues with the programming assignments and the lectures could do with some more practical examples. Be sure to check the discussions forums for help. For me they were essential to passing the course.

This was a bit more challenging than the previous two courses. I didn't enjoy it as much as the previous courses, however, I learnt more than the previous two. The discussion forums were helpful and the instructors contributed regularly. The assignments were a bit frustrating at times but still manageable. However, the assignments could have had a bit more of explanations.

Very good course, interesting material. However the amount of programming knowledge required is way beyond a beginner like myself and I struggled with that , consuming much time. Those with programming knowledge have a tremendous advantage on this course.

There should be a week or a separate brief course on python/numpy.

A follow up more advanced course would be good too.

It was a harder course where I spent double the time I have initially anticipated.

It is much harder than the two predecessor courses in specialization, and amount of direction when it comes to doing exercises is significantly smaller. More Python knowledge is required.

That said, I feel like I have finally understood the PCA and math behind it, which made it all worth it

Loved the course, although I wish there was more ramp up to some of the complex scenarios (or anything simple but new). Very helpful forums/community. Requires a fair amount of external reading/referencing for some of the concepts which seem to be covered only at a high level in the lectures.I would love to see more courses on applied mathematics for machine learning.

The best of the 3 courses. This is a refresh course of course. A solid background in linear algebra is required in order to fully understand everything. I personnaly recommen the MIT course from Gilbert Strang before you try this one. The python exercises are very well designed and I can only be thankful to having shared this knowledge. Thank you Imperial College.

Not quite as good as the other two courses of the same specialization. Even though the instructor seems immensely knowledgeable he could work on delivering the material (which is more abstract than before to his credit) in a clearer manner.

The programming assignments are great albeit a bit hard to troubleshoot at times. All in all still a great course.