This course would be immensely helpful for those who have not started on their first machine learning project. However, the insights shared are quite commonsensical and intuitive for those who have already had some minimal experience in machine learning. This course also does not feel as substantial as the other courses in the specialization, though the tips provided are definitely valuable.

I recommend this course. This will be a bit of theoretical which is good. It will talk about real world scenarios over the errors which is what we deal in day-to-day life and how to deal with it.

more practice!

Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.

I'm glad I spent some time on the "Flight simulator" assignments in this course. It's the first time in the specialization when I actually found the quiz questions challenging, and that's a welcome change. However, I didn't learn too much from the lectures. They were too repetitive, either repeating themselves or the material from the previous course. One or two videos could also do with better editing work: I could hear Andrew making a soundcheck, and there's a 30sec segment that's played twice in a row. Overall, it's probably worth doing this course, given that it requires very little time, and the assignments are useful.

I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

A good course but good get better

Nice taught me about specific structuring implementations which needs to be checked in case of faults.

This course is not so well organized as the previous two. Everything else is very good!

Lack of practical assignments

This is a great course, something I will keep coming back to even after I'm done because it talks about strategy and rules of thumb re: Machine Learning/ Deep Learning approaches. It introduced me to certain concepts that were brand new for me and that was a great outcome for me. I wish the audio was better and the notes were better because writing on the small screen really hinders expressibility. I would rather have Dr. Ng write/draw on a chalk board than the small screen, I feel it really constrains his process. Still it's a great course!

I see the point, but takes too long to make it. But part of a great series.

This course is very helpful to learn best-practices and problem-solving strategies that can help improve our deep learning algorithms. While I think the ultimate way of learning is through practice, here you can at least get a list of things to try in the future as you work on these algorithms.

The explanation of the concepts is very easy to understand, and the assignments are really helpful.

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.

a very practical course!

Really very helpful

It is good course to understand real world problems.There is too much theoretical knowledge and exposure but sometimes it is boring also.

Too good and more informative course in NN

Very valuable insight in real world usage of DL and guidance in approaching DL projects

Kinda boring, but still pretty practical.

Another really useful course on Neural Networks that will help improve your results, and it's only 2 weeks.

This course was very helpful.

It is a useful Content to keep in mind. Not as practical as other contents in the Specialisation. But, as Andrew Ng pointed out, it is a topic many times overlooked and I'm glad it was discussed.

really awesome course !!!!