This case was useful for getting an experienced way of approaching the real-world problems of ML. The quizzes further added to the application of the basics learnt throughout the course. Overall, it was a good course!

very much wonderful. especially the simulation process, which extracts the pure logic decision process during implementing DNN without actually experiencing all the detailed procedures which are not really challenging

Good course. Very interesting!

Having done the course, most of the ideas seem fairly obvious. However, the chances of me coming up with them on my own are almost 0.

Therefore I think the training has been successful.

It was an amazing experience and I learn a lot of new Machine Learning strategies and error analysis techniques that will help me a lot in my future research work. Thanks a lot, Mr. Andrew, you are an awesome speaker.

Excellent course, give great practical advice of how to structure projects and to make decisions to improve you models. Those insights are hard to find elsewhere and it's the most valuable contribution of this course.

I found very useful this course. It is different of what you'll find anywhere because of its focusing in the strategies for developing deep learning projects. Andrew share his experience in a short but unique course.

I really loved the learning of different ways of error analysis and solving issues based on the outcomes of the analysis. This is really a ready to use knowledge for me to implement in my job life. Thank you so much!

I would give 4 and 1/2 star because I don't understand some of the questions I missed. I will ask in the forums for more detailed explanation. This is a nice course for a simpler break in the middle of the AI course.

Very nice to get the advices from NG. Wu, But I think it's better to learn this lesson in the last stage when you have a basic understanding of DL and the strategy should be useful when you debug with your DL model.

Excellent. This class gives an overall perspective on how to approach ML projects to ensure that efforts are focused in the right areas to solve problems where the solutions will have the most impact on performance.

Dr. Ng set the bar very high in the previous two courses of the specialization. This course is also excellent with very useful practical advice, but maybe a little less polished and streamlined than the previous two.

This course is unique in content and you cant find anything like it anywhere else.

The amount of experience that Andrew conveys is enormous and practical tips that only can come from a real professional like Andrew.

The course by prof.Andrew Ng gives us a great insight on error analysis and strategies to apply when building a machine learning project to achieve or surpass human level performance in applied deep learning tasks.

I have learnt lots of things on how to structure my machine learning project. I hope that the course wold indeed be very helpful for me in future in my endeavors into fields that are using DL as core technologies.

Thank you very much, Andrew Ng. Your course is very cool. It helps to understand better how to handle different tough things and learn very fast. I recommend this course for learning and getting into Deep Learning.

I think it is a really nice qualitative course which really broadens your perspective about various dimensions of a NN project. It is very eye-opening and very conceptual and honestly, very practical. Thanks Andew

thank you for this course, your efforts help me achieve my goal of understanding machine learning and how to apply it to real world and ways of teaching is constantly building my interest in this field. thank-you

Valuable insights into how to structure ai projects with the respect to data, new data, buggy data, synthesized data, mismatched data, and much more such as error analysis and how to use pretrained neural network.

A great course where Andrew set the bases for a new way of doing machine learning. Aiming to standardization and improvment of ML life cycle to bring Deep Learning model in production much faster and with method.

A very informative course. Many unknown concepts to improve an NN are covered. I have gained a lot of clarity about the various levels of error fixing and proper training of DL models. Wonderful job by Andrew Ng.

Great course! Content was very interesting and did a good job building upon the previous courses. Enjoyed the assessment in the for of the simulator, gave me a good sense of real world Deep Learning applications.

Teaches how to debug a lot of design and implementation issues that happen when going from theory to practice. This is is the kind of knowledge that you can get from Prof. Ng's experience, but not in a text book.

I would like to thank coursera to giving me opportunity to follow Structuring Machine Learning Projects course. In the last few weeks I learned a lot of new theories and basics of improving deep neural networks.

In this section I learnt about the theoritical part of ML strategy, how to set goal, compare with human-level performance, Error analysis etc which I think will help me to develop as Machine Learning Specialist.

This is a great course but I think it'd be even better to place it after Convolutional NN course. And also wish that there were coding assignments, too, as in other courses in this specialization (Deep leaning).