The course appears to be in development and could be strengthened with programming assignments that take you through an actual mock project. Otherwise, the current content is enjoyable.

Not so much different with the materials in the Machine Learning course from Prof. Andrew Ng itself. If you don't have the time to finish the ML course, then you should take this one.

So thanks for your well-designed course. I would say that if you provide much examples or programming assignments for this course, it would definitely be more helpful for the students.

A good approach to ML strategy. However, having a programming assignment to better explore results from tweaking models based on the strategies discussed in the course would be great.

Developing intuition on how to structure projects in deep learning is essential to becoming effective and productive. This course is a good start for gaining that experience quickly.

Valuable information that is well-organized and clearly delivered. Would benefit from a larger number of shorter exercises each week to cement learning after each group of lectures.

This is a great course with excellent contents and guidelines !

Point for improvement:

Please add a programming assignment in python and the questions appearing during the lecture....

This course provided an empirical approach in tackling hurdles in solving most common issues faced by data scientist in solving Machine learning problem in a very simplified manner.

I really appreciate learning about the high level strategies for designing machine learning projects. I only wish there were some programming exercises to put it into practice.

Based on my own experience and comments on the discussion forums, I get the impression that the quizzes have a couple of errors in them that makes it impossible to achieve 100%.

This course is less pratical and theoretical. I don't mean it is not helpful to me. I think this course might be helpful as guideline when I hand on the real project in future

very good course, but I felt like it was lacking one more week of course to get deeper knowledge about how to really get data sets and how to set them up for real applications.

While the course teach valuable concepts, the material (especially the quizzes) contained many problems, ranging from grammar mistakes, to ambiguous writing, to wrong answers.

you may think because the course is shorter will be much easier but the videos has a lot of information to process. I am excited to tried this techniques on real applications!

A good course. Provided general high level thinking and reasoning for quick problem solving, data management, multi-tasking, transfer learning, and error reduction techniques.

Not as exciting as the previous courses, but informative nonetheless. A section for handling imbalanced or skewed datasets would be useful, especially for multi-task learning.

Not as deep into details as the two first courses in the specialisation, but nevertheless I learnt a lot of techniques that I hope will be feasible when I work on AI projects.

Excellent lectures and notes as always. Great insights and clearly explained. I think we could have used a programming exercise on transfer learning at least in this section.

While the course content is very good just like the previous 2 courses of the specialization, quizzes on this course have little language errors that make the questions vague

More coding exercises could be included with much more mathematics background explained. Videos could be made a little shorter. There is redundancy in the some of the videos.

Good overview of how to structure ML projects with great practical advice. I wish the course had includes a programming lab to help us try out and practice some of the ideas

This course is pretty good. In this, I can learn about real world situation on developing machine learning projects. But I think, it could be better if has some code (Maybe)

Good content. The tips and tricks a experienced AI practitioner has was shared. But at least one programing exercise applying all the concepts learnt, would have been great.

In the first quiz, the comments about why an answer is correct are too simple. On the contrary, in the second quiz, the comments are really good and give us better feedback.

I feel more Examples should be given regarding the variable and bais tuning, also Error analysis videos should be a bit in-depth. Everything else is as good as it can get :)