This course gives a bird's eye view of a ML project ABSOLUTELY needed in a Production environment or consultancy. Maybe this one could be the first or second course overall..

Most important learning is on how to make acceptance matrix and validation matrix and setting goal for team. And most importantly error analysis and strategy for improvement.

Very learning-ful and insightful. Andrew - as always - is a great instructor, explaining all the topics in a clear, understandable, concise and ready-to-use/pragmatic manner!

This course was pleasure to complete. I enjoyed it the most of all the three so far! And some of the stuff taught in this course felt so useful even outside machine learning.

Great overview on how to review the output of your project, understand the results, and how to iterate (what to go after) to make your algorithm. Andrew Ng nailed it again!

Excelente curso, se ven temas que no se ven en otros cursos. Lo disfruté. ¡Gracias!

Excellent course, topics are seen that are not seen in other courses. Enjoy it. Thank you!

This was a short but very useful course - the concepts will be highly useful for future projects - especially the testing method using the scenarios of applied Deep Learning

Having dived into the detail, this is a great overview to help you string together your new skills and implement something. The "flight simulator" concept is a powerful one.

This course is one of a kind. It offers DL new entrants the opportunity of understanding applied DL and gaining 'offline' DL experience. Thanks for the opportunity given me.

Great course. Provided important insight in how to setting metrics, evaluating errors, dealing with mismatched data and an intro to transfer learning and mulit-task learning

I've learnt the coursera even not watching the last two videos, maybe the order is wrong. But the course is very beneficial to diagonize the pathway to kick the problem off.

This course teaches very good practical advice for designing your own deep learning project, prioritizing resources, and identifying weak points in your deep learning model.

Fantastic course, Andrew is a clearly a leader in the field and presents a very in-depth but approachable course. Very much looking forward to RNN and DNN (modules 4 and 5).

This course is packed with useful tips and practical advice about issues that will arise in ML projects, making you think about real-world scenarios and how to handle them.

Excellent meta view of how to approach training, validation and testing which is not really available elsewhere as Andrew has actual experience of true DL projects at scale

Excellent and insightful course. The only thing I would note is that in my opinion I would not classify it as "beginner" but more of an "intermediate" level course. Thanks!

The course is laid out well and the videos are very clear and understandable. The instructor who creates the videos, Andrew Ng, does a very good job at explaining concepts.

Great course. I now have a better understanding of how to approach a project and how to make better choices on what to focus on to continue improving deep learning models.

Simply loved some of the tips shared by Prof. Andrew Ng in structuring a Machine Learning problem. The simulation Use Cases were helpful in structuring the thought process.

This one was very rewarding. It looks simple but really challenges your mind to think strategically about how to plan and prioritize for ML projects. Really useful content!

This course is very important: it will lift you up from the pure coding problems, showing you how to actually develop a project, take decisions and work in a team.

Great job

enjoyed it, and provides a more qualitative sense of how to assess machine learning projects which makes a nice complement to the other courses which has more programming.

Very good image about ML projects as a whole thing. Perhaps this course doesn't have any programming task, but but it has the good recomendations for building ML projects.

I have some knowledge on this specialization , but this course is also very helpful for individuals who do not have insights in this area. Thank you Coursera and its team.

Top course. However, the course could be improved by having the students actually develop some projects and facing the issues discussed in the course in some real project.