Not much to learn in this course, basic recommendations can be condensed in one or two lectures

Too short

the best course in so far, not that much theory but a lot of "insides" from the field. However, still no practice, Im studying for 3 month and still have no idea how to create a real application.

i missed practical (programming) assignments here. quizes are great, but could never substitute for getting hands dirty.

the course doesnt have any programming assignments. I feel that these two weeks should have been added/combined with first 2 courses. The knowledge that is provided is useful, but it is mainly useful once you are an expert at building neural networks and models. I feel that this course should have been the last course in the series instead of the 3rd course

Quite boring and not so interactive like the first course

I found it quite verbose... Could have easily been shrunk and fit inside the other course... Don't think it needs a separate course for this

The material is great, but the production quality is so poor that I had to give 4 stars only. Videos have blank and repeating segments, and more quizes have mistakes that make getting a 100% because you know the material impossible (you have to tolerate some wrong answers to do it). This means you can't rely on quizes at all, because maybe the ones you got right were actually wrong :). The ones I got wrong were also called out by other people on the forums, so I guess maybe I am right.

The weakest of the three so far - comparatively lots of fluff. Unclear definitions with lots of perhapses and maybes.

This course is too theoretical, would like to see some multi task learning or transfer learning programming assignments.

Very verbose with hand-wayy examples. The 18 minute lecture was the hardest Ive tried to not fall asleep. The second quiz has extremely badly written questions with multiple choice answers. Very ambiguously worded QnA. Don't mistake this review for the whole DL specialization though. Andrew's DL specialization course is brilliantly structured and an excellent primer for folks such as myself just getting into DL. It is only this section on structuring ML projects which is a little bit of a drab.

The 3rd course is more art than science. There is a lot of breadth, but we cover each topic in passing. Therefore, from a student perspective, I find that the concepts are not cemented and it is entirely possible that I forget them once I move on to the next course.

The second issue I find with the course is that there are no programming assignments. Programming assignments. Programming assignments are key to understanding such complex topics and getting the idea cemented. It would have been much better, if we could cover each topic such as data-mismatch, comparison to human level performance, etc via assignments.

Answers in the multiple choice seems incomplete for me, I don't necessary agree with the answers presented unless more detail on some context (that I don't think we should assume) is present for the questions.

Really bad course. Even the professor does a good job at explaining everything, it does not seem to be a technical course :(

Had the feeling of a "filler" course although it was interesting to hear about the various challenges

A lot of the content seemed redundant both within the lectures and with the previous courses

Too much information in too little time. Additionally, all information is mostly practical, and having no real exercises makes it hard to remember all the details.

Just a few videos without any programming excercise or a bunch of rather broad statements that are not really tried out in programming examples are not really worth the money and more importantly the time. The first two courses are good, this is definitely a drop in terms of quality. This one needs more meat on the bone.

Definitely not worth paying for (and I literally completed this in one afternoon). Thankfully I did not pay, so it was not that bad value in fairness.

In honesty the lack of value from this course actually says a lot about Andrew Ng's original Machine Learning course, which was consistently excellent. Actually coding in Octave for that class cemented a lot of concepts as well, which this course does not.

The title of the course suggests this is pitched towards more advanced students who already know about Machine Learning but maybe not so much about best practices. This feels far too basic for that demographic. The practices are sensible though and useful, if maybe overly focussed on massive datasets as opposed to the ones that Google *doesn't* deal with on a daily basis. Things like SMOTE could have been mentioned as well, for example.

TL;DR: This feels like a missed opportunity. My advice is don't take it if you've done Andrew Ng's ML course. Google things after that and wait for a decent course that's pitched towards intermediate students.

Too little materials.

Much too slow

Pretty basic and not enough practical

The most exciting part of the course as others in the series is the interviews that Andrew does with deep learning researchers. I thought I would learn more about how to structure actual machine learning projects from a software perspective and how I would incorporate them to real products. I felt the videos for this course were too long and cover somehow basic common sense.

There's theory, but, without practice and application in my opinion. I did not like it because it seems to be easily forgotten seeing that I did not associate with practical excercises.

Missing a bit of practical Python exercises