Good course with a lot of qualitative information that is quite useful. Giving it a 4 because it would have been great if there were accompanying Jupyter notebooks. It's a solid course overall and I recommend it to anyone interested.

This course is highly intuitive, practical and less mathematically complicated. Prof Andrew Ng uses many examples to elucidate concepts. Post learning one will be capable of choosing which direction to go in solving an ML/DL problem.

This was a great lecture from Andrew Ng. I have learned basics of error analysis, multi-task learning and structuring a machine learning project in general. This will be very useful staff for my professional career. Thank you Andrew!

I consider this course is a truly gem because of the bunch of good practices every person getting into the field should be aware of. I also liked how quizzes were designed to address industry use cases and not just academic details.

Excellent point of view. many teach you how to do /write code to apply ML to your problem. in this course I felt they were teaching me how to understand the results and how to improve it. Extremely interesting for potential Managers

This course is exceptional since we can learn a lot with Andrew Yang's great experience with Machine Learning Projects. It'd also like to suggest to add new classes about powerful and newer techniques, such as feature visualization.

It was one of the more thought provoking and excititng courses in the specilization. Although at first it seemed simple, the case studies illuminated how certain minute details can derail a project and how to avoid them. Thank you.

These are most toughest things, in which people takes 100 of hours to explain and still people confuse. But by doing this course within a 4 hrs span you will have a decent knowledge. Kudos!! to entire team and thanks a lot AndrewNG

This course covers lots of practical advice and techniques resulting from real world project experience by the author. I highly recommend this course to anyone involved in deep learning projects, even if not in a technical position

this course very important other than previous courses because we need to understand the data and split the data set across the train, dev and test and making strategies for training the dataset using model. Thanks for this course.

Great course with some awesome insights into structuring the analysis of machine learning models. Definitely picked up a ton of strategies, tips, and tricks that I will be using as a I move forward with my machine learning career!

The mindful advice does not only deepen your understanding in deep learning, but also stimulate creative thinking in my own PhD research in a total different field. It is also enjoyable to watch the interviews, which I favor a lot.

very helpful!

Andrew teaches a lot of knowledge that can't find in books.

This kind of knowledge is very helpful for student when he gets his hands on real-world ML project.

Error Analysis & Bias& Variance are all useful techniques!

Very helpful! The experiences of building deep learning systems might be very difficult to obtain through other classes. When learning from practice, however, the costs are too high. Thus this is a really marvelous course to take.

very good course and I believe this course will save lots of learning and experience years by directly guiding you on how to structure ML projects and show you different ways to solve different kinds of problems that will face you

Dear Prof Andrew Ng, grateful to you and your team for yet another excellent course in Deep Learning specialization. ML Strategy teaches us important practical aspects which are absolutely essential for the success of ML projects.

This course give a substancial idea of how to deal with Machine Learning project.

It is only a two week course with 3-4 hours per weekd but is at the same price as the others.

Though it is still an excellent course that I recommend.

The basic concepts of structuring machine learning projects help improve our skills. This course really helped me build my project correctly. Extensive information is given in simple language. I recommend this course to everyone.

Again a very good course by Andrew Ng. Concise, structured, easy to follow. The quizzes are a highlight, as the questions mimic a real project's journey and ask about exemplary situations (instead of just definitions and jargon).

This course gives insight on how to use deep learning algorithms to use in real world. Quizzes contain really good case studies which are very good. Definitely recommend this course even to one who knows deep learning algorithms.

Excellent course that covers some of the most critical aspects related to machine learning projects. The approach used in the quizzes is very effective. It introduces learners to some common problems under a variety of scenarios.

This was really interesting but at the same time quite tricky to take any decision after we get the poor results. I think it would be even better if we had also programing exercise where we could for example do transfer learning.

'Structuring Machine learning Projects' is a strategic approach to modeling machine learning algorithm. And this is the course that really teaches how we can apply ML algorithms we learnt to the real world problems "Efficiently".

Loved the Course, it really Helps a lot for the Projects, You save us lots of time.

I have a question which activation function did I have to use on the final Layer on a Multi-Task problem, Im working in a project with multi-task.

The simulator exercises are particularly interesting, as the introduce learners to making decisions when dealing with machine learning projects. This was the highlight for me, and I believe, it has help me build some intuitions.