I got the same feedback for many of my colleagues that this course is not that important and I should start with course #4 instead, but I am glad I didn't there is a lot of insights and experiences in this course that I think it would take anyone many years to conclude by himself.

That's a great course to learn some practicalities of deep learning/transfer learning and multitask learning, and when to use different strategies for structuring a project. In my opinion, the course could do with a hands-on programming exercise to help consolidate the learnings.

Sometimes its become bored who has not any experienced into working on real life ML project because without facing problem you can not understand problem in better way so i recommend course instructure to make this course with little more practical way so that it easy to digest.

The lack of progamming assignments hurts what could have been one of the best courses of the specialization, especially in solidifying the advices and ideas seen here. Nevertheless this course still provides valuable informations, and it's one i'll come back to later for sure.

A bit slow going and repetitive (and some simple video editing to remove double sections would improve things). Nevertheless I'm amazed how much I learned or consolidated is just a few evenings of watching these videos. Thanks again! Looking forward to course 4 in this series.

Thorough and practical guidelines to structure and analyze issues with machine learning projects. Distilled learning presented from a lot of project experience. It would be hard to gain such knowledge without having gone through a number of projects. Accelerates your learning.

It was a very informative course. I learnt about different metrics that are used for measuring the success of deep learning models . I learnt about the different approaches like transfer learning, multi task learning etc. The assignments were very challenging and interesting.

Machine learning simulator assignments were great, wish we could have more of them both in this course as well as in the other courses in the specialization. Additionally, I would have loved programming assignments that reinforced these largely workflow-related concepts.

Useful knowledge regarding the efficient practices in the application of machine learning. Mentors doesn't seem as responsive though, compared to the other courses of the specialization. Quizzes were helpful, but needs more justification for some of the correct answers.

The course showed the experiences while dealing with machine learning projects but could have been better if the experience would have been shared through practical exercises rather than objective case study.

It would be better if there were programming exercise as well.

I have a feeling that this third course is not yet fully edited. I see some black screens, and sometimes the clips have Andrew speak faster than usual. Nonetheless, the various tips and appropriate actions to take when doing a machine learning project were very useful.

Good explanation for the initial steps of organizing the ML project and the direction to approach the problem accounted for. The quiz was interesting but as it is the same set of questions for any next attempt, I would not say I have mastered the course completely.

Only 2 weeks, good concepts to know. But videos are not "final release" they are not well edited. Some time Andrew repeat the same sentence 2x but they forgot to cut it.

No programming assignment. Although quiz format is fun and you really learn by doing the quiz.

This was a slightly more theoretical course than the first 3 in the Deep Learning specialization and, even thought I enjoyed it, I think the info would stick better if there would have been a programming assignment too (or some other type fo practical application).

Nice and concrete examples of what to think of and focus on when trying to improve your machine learning projects. Not as engaging tasks to complete as in the previous courses in this specialisation, however a good change of scenary if you have been doing these.

Great material, but it's not quite easy to understand it from scratch, if you didn't have such problems yourself (i.e if you have no experience in deep NN training). I've stored this material and going to revisit it after I gain more experience in training NNs.

Great course, however the quiz of week 2 had some ambigious phrasings and I think at least one example (the one with the data synthesis of foggy images) is contradictive of what was taught in the video lessons. Other than that, really good content and teaching!

A lot of concepts were put forward and taught well. If there was a programming assignment as well to back up the concepts that were taught like multi-task learning, how to deal with data mismatch, dividing the total data into train\train-dev\dev\test data etc.

I feel there is a glitch because even after completing the videos, it is not shown as completed and I had to replay them multiple times. Also there is a glitch in the assignment, because the correct answer in one attempt is shown as incorrect in the next try

Great and easy-to-follow introduction to structuring machine learning projects and focusing on what to tune on neural networks. One star left out because the content has not been polished, but there are minor errors here and there with separate corrections.

The course seems a little less concrete than the others in this specialisation. But nevertheless, still a useful building block in anyone's deep learning repertoire. And note it will probably take less time to complete than the others, so plan accordingly.

It would be nice if there are hands on assignment or small projects on fine-tuning with existing weight you can found in the internet or multi-task learning project. Overall, it's a great course with many useful technique to try in the real world projects.

The course was really great, but a little part of the content was repeated from previous courses of the specialization. Also there should be more quizzes or exercises to master the large amount of practical advices for managing machine learning projects.

Very good class! It really hits me that AI programming is a different paradigm. Managing data is key.

That said, the materials in week1 have quite a bit of overlap with the 1st course (NN & DL). The materials should simply do a quick reminder a move on.

I liked this course, but not as much as the others. It is however setting the foundation for the remainder of the course material. It carries with it wisdom, which I think will make more sense at a later point when confronted with real life challenges