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.

This course provided some interesting strategies and advice on how to start and struture machine learning projects. My only complaint is that this course should be placed at the end of the specialization. I felt like I wanted to know more about deep learning models before I learned how to strategize on deployment.

Was extremely helping in providing ideas on how to start and work on machine learning projects. Provided clear and well thought out ideas on how to make the most use of time and data. A small improvement can be made to the course by dividing some of the contents of the course to another week for better structuring.

Course is great. All concepts are explained very meticulously. Lots of respect for Andrew NG. Just a small suggest please don't give more examples on cat classification. Autonomous driving case study was good, speech recognition examples are good. Please give more realistic examples, that can be used in interviews.

Great explanation on how to structure your machine learning projects like distributing data among train & dev/test set then what to do for each type of errors to continues to transfer learning, Multi task learning, End-to-End Deep learning. It has been a fantastic journey learning about these different techniques.

There were definitely useful pieces of information in here, but I think it could have been condensed and delivered as part of the previous course. I liked the flight simulator quiz approach. Sometimes the wording of the questions was tricky and that may be causing people to get stuck even if they know the material.

-You should edit better some videos, in some parts Andrew repeated what he said, or there were long silences, or also what he was writing wasn't in tune with what he was saying.

-I'm not sure if the topics covered here justify a whole course. Maybe the insights shared here could have been inside some other lecture.

The flight simulators' results were not consistent with the advice provided in the lectures. I'd suggest being either less black and white in the simulators' answer responses, or, being more polarised (more black and white) in the advice provided in the lectures. Otherwise, this is a 5 star course. Many thanks!

I liked the course but it contained quite a few glitches which could be easily removed to improve the overall experience. E.g., once Prof. Ng makes a long pause and says "test". Sometimes the same ending is placed twice or in the final "Heros of Deeplearning" video Prof. Ng seems to ask the same question twice.

this course is pretty different from other courses in this specialization. It gives high-level knowledge of machine learning instead of implementation details. The course content is useful but it seems a little boring to me because I can't do any fancy, real machine learning projects as exercises in this course

The quizzes were actually a great idea. The content is definitely useful, as I've had similar difficulties in my company. I felt the videos took much more time than they should, though. A lot of the content could have been resumed in shorter videos. It was the first time I actually had to accelerate the speed.

Good materials.

This course was really short though. It seems to be a bit artificial to make a "specialization" out of these courses, where they could easily also fit into 1 longer course. Fortunately the dates you can start the courses are flexible enough that you don't need to wait (too long) between courses.

Excellent information about how to diagnose errors during machine learning and complete projects well. I would have liked a small coding aspect to see how certain concepts (eg. train-dev set, transfer learning etc. are implemented), even some very basic examples would have helped. Overall still a great course

Good course to get started with Machine Learning, the introduction video could have used simpler languages though as many of the jargon might not be familiar to newbies (therefore scare us off!!) and they are really not necessary prerequisites to the course. I enjoyed the quizzes as they are real and useful.