I wouldn't really consider this a "course," but the stuff he taught was great. However, Andrew could go much deeper into these topics. Some real data examples that he has come across would be even more helpful. Seeing how he codes his approaches in python would also be a very useful (and quick) batch of lectures. If he needed to extend it another week that would be understandable.

Excellent course just like the previous two. Short programming exercises would have been nice to have. Some of the answers in the quiz were too similar and this might be quite confusing for non-native English speakers and therefore can reflect more knowledge in English language rather than knowledge in related machine learning topics. I am looking forward for the next 2 courses.

This could be a more useful course if it came after convolutional neural networks and sequence models courses. In that case, the learner could practice some of the strategies on the models that he had previously developed. Right now, most of the strategies will be forgotten by the time that the learner reaches to a point that can confidently develop large deep learning models.

Great material that's clearly based on valuable practical experience. I and found the "machine learning flight simulator" quizzes to be an educational format. However, the editing of the quiz questions (grammar, matching question types with wording in the question etc) was not flawless in September 2017. This course would benefit from another review pass from this perspective.

This course is clear, and show how a machien learning project should be driven. But there is two problem : First it is entierly theorical : no pratical exercices (so it is only 4 stars) ; second it did not speak of a big problem : How make your boss understand that if you use the "test" set too mush, it become another "developpement" set -> without using sciences words...

Overall, a great course for designing deep learning projects, which gives a lot of insights and tips that typically not taught at university classes. However, there does exist some minor problems related to video editing and quiz problems. I suggest the lecturer or staff of the course put more efforts in dealing with them (and maybe attend more to the discussion forums).

This course gives an overview on how to address common problems faced during machine learning projects. Although these experiences can prove valuable, for average people that may not be actively involved in machine learning, the information may sound like "common sense". The course may benefit with a more abundant set of real-world practice scenarios for analysis.

This course was totally different from the previous two courses. It was focused more on the theoretical aspects of how to approach and build ML projects, difficulties that ML engineers can face and how to avoid them. The content of this course could have been more interesting if more real world problems were included and if there were some programming exercises.

A very nice course to teach how to start a data science project, how to evaluate it, how to select path ahead improving the model, what all to be taken in consideration before training or while training or after training.

Some more case studies could be added since the course is smaller in length and case studies are helping a lot in making understanding clear.

Again, great course. Congratulations. This time, i've missed some programming assignments, although the case studies was very instructive of the practice, some programming experiments with transfer learning will be great. Nevertheless, the course has extremely valuable knowledge to those, like myself, that want to practice in real problems and corporate world.

Actually adds some insights I hadn't learned (or at least I was guessing but it's always nice to have a double check) after 4 years as a data scientist.

Also, some of those insights are very specific to neural networks projects, so doesn't matter how many years have you been working if you've never made deep learning projects this will help you nevertheless.

This two-week course gave very important concepts. However, there's no programming assignments and lectures are lengthy. It felt a little "boring" for a hand-on guy like me.

That being said, one should not skip these important lessons if he/she wants to take charge of ML projects one day instead being a programmer who only takes orders from others for life.

This course gave a lot of practical advice and is excellent material to combine with the more programming-focussed lectures of the deeplearning.ai series.

Small points of criticism are that I thought some videos could have been a bit shorter/less repetitive and there were quite a few language mistakes in the quizzes (missing words and grammatical errors)

Course 3 of the Deep Learning Specialization. There is no coding in this one but longer quizzes which require you to fully understand the concepts and recommendations given in the course. It's all about ML project strategy and how to manage you results and errors. Quite interesting and important for the general understanding of a Deep Learning project.

I liked this course overall and found it to be very informative. I, personally, was a little thrown by the eclectic nature of the course's materials. Sometimes it seemed as if the material covered in each week was only loosely related, or was thematically similar for part of the week, but then the last few videos were on something else entirely.

Course content is great and unlike other courses in the specialization this would be a good course for a non-technical manager or product manager. The reason for 4 stars instead of 5 is there are only 2 weeks of material. Some of the topics that should also be covered in a course like this are: ethics, regulation, compliance, and copyright.

It is too much of theory, with significant repetition from machine learning course and within Deep Learning course 1 and course 2. It would have been lot of help if we had programming exercise on transfer learning, data synthesis and multi task learning to get a hang on practical experience, similar to first 2 courses of Deep Learning!

thank you for to teach how to research and it will be of great help to real researchers.All theories have been a pity not try because I did not get a lot of the actual study. I think it will be a great help for future research opportunities.It is very difficult to study because it is not practical. but in future it will very helpful.

Although purely conceptual, the course really gives good advice on how to come up with a work flow to react to errors due to data, and the metrics that can be used as reference. I just wish there was an assignment where you could see a NN working with mismatched data and how it reacts to some of the improvements that were mentioned.

Course contents are great as it talks about how to improve performance by giving real world example. This is one of the most crucial pieces in any model building task, but still is less focused in traditional courses. Andrew Ng's team has dedicated a full course on this aspect, which I believe will do the learners a huge benefit!

The quizzes were a little annoying to get through, as it is not much about deduction or reasoning, instead it's about learning the advice or rules mentioned in the videos. I think an actual implementation of a learning project and applying the error analysis, transfer learning, etc, would be more beneficial for the student.

Gave me a broader and more strategic perspective on how to structure and run a Machine Learning project.

I just felt this course came too early in the learning process. It would have far more relevant and useful had it been a more downstream course.

This does not take away from the fact that the content is very relevant

The content is great, but there's much to improve on the Quizzes: many questions are ill formed and ambiguous (unnecessarily in my opinion, since they aren't testing your understanding and thinking, they just feel artificially hard and you end up just trying to imagine how the instructor is trying to deceive you XD )

Material is great. Quizes could be better imo. Sometimes I get the questions wrong not because I don't understand the materials but because of the choice of words in a question. Also would be better if there are actual real world case studies instead of just going over the concepts of how to structure a ML project.

This was a good course because it "kind of" prepares us to real world projects and we think about what to do when different problems arise. I would also really like if this course included a section on how to create datasets from images, sounds etc. and prepares us for the "boring" parts of machine learning as well.