Prof.Nag and Team, had really gave immense effort to make things brain friendly. Really appreciate the effort to make this so easy going, but conceptually very high content. Recommend not to finish over night, but trick is to go slow and grasp the content.

This course is very important as it offers solutions that don't exist in literature to tackle real DL problems. Andrew Ng is basically teaching you from his vast experience. I highly recommend it esp. for those who want to design / implement DL products.

This course teaches materials that are missed or thrown away in other courses, but they are important detail in doing research or even field application. It really outlined important troubleshooting steps to take to get the best performance of your model

Outstanding course with immense amount of real-world cases from industry. However, there is no programming tasks here in this course and I was feeling a lack of programming assignments a bit. But overall, the theory and case studies are just incredible.

I think despite being more theoretical course than the previous ones, it is still one of the most important courses in this specialization as we learn about how to handle a real life project and mitigate the problems that arise in a more systematic way.

The content is super useful. I have struggled in my previous projects with many problems discussed in this course. It is great to hear Andrew Ng's opinion and his suggestions will definitely help me push the next project better into the right direction.

I think this course covers the cases where I tend to bog down and waste time, and has provided me with useful and practical guidelines to get out of them. You should not underestimate the value of this course,

just because there is no coding assignment.

In this course, the instructor shared various methods to point us in the right direction of where we should improve our model. Also, many new techniques were also discussed in this course which help develop accurate model even with fairly little data.

Helpful context for a person such as me who is changing careers to the Data Science field. Would be easy to focus only on the mechanics of ML/DL and ignore the broader context of how to really pull a ML project together and make the effort effective.

This part of the course is really unique and provides an understanding of what are the challenges that you could as a Machine Learning engineer. The problem of the exercise was really great in terms of planning and execution of the real-world problem.

Excellent overview of common pitfalls and problems you might encounter in a machine learning project. The lectures use good practical examples to highlight the issues. I definitely gained a better understanding of how to set up and run an ML project.

This course is very good at establishing the fundamentals of 'problem analysis' - something which a lot of analysts actually struggle with. I enjoyed it and found the examples helpful to think through the various steps and types of ML applications.

Learned a lot about dealing with datasets where training data and test data might not have the same distribution. In a practical deep learning project, a lot of decisions are strategic and this course will definitely help in making better decisions.

Another great course from Andrew Ng. You will learn how to manage deep learning project and get to know some clever ideas of approaching the project from managerial perspective. You will also get to know important people in deep learning community.

Pretty important course in my opinion. I had skipped this course last year when I was new to deep learning. One year later after working on some deep learning projects, I feel this course makes a lot of sense to me and I wish I had done it before.

I do not think you will find this expert advice elsewhere. And the extended scenarios which the quizzes test give the feeling that I now have real experience (although I do not) making high level decisions about guiding a machine learning project.

An excellent overview of a rarely discussed subject. Often lost amidst the seemingly daily discoveries of new deep learning tricks is need to apply deep learning in real applications. This course did a great job of addressing the latter concern.

One of the key courses in the specialization besides being short and tricky. The content of this course is exactly that which differentiates between a mere programmer from a data scientist or a machine learning engineer. Do not skip this course.

At the very beginning the course seemed to be about self-evident, even trivial things. Now I can't imagine it to be dropped out of the specialization.

Thank you prof. Andrew Ng, you gave us a roadmap to move and saved from many blunders on the way.

Great teachers make great students. Exceptional how Mr. Andrew explains everything and gives real world examples to relate. and the community is also very helpful, you keep learning new things and understand problems just by reading the comments.

A very insightful course detailing the problems encountered in a real-life deep learning pipeline. The solutions were very intuitively explained and the case study approach reinforced the concepts. The heroes interviews too were very interesting.

This course give a lot of insights about approaching a machine learning problem. It doesn't contain a lot of concepts or algorithms regarding ML itself, but the course will be very helpful for anyone interested in working on real life ML systems.

A really must have course for anyone who wants to enter the field of machine learning. The concepts being thought are crucial for creating effective solutions in the modern world. Andrew explains everything in a simple and down to earth approach

This course provides very specific information about best industry practices. When I started it I was ready to underestimate its value, but by the end I realized Andrew Ng provides many insights that one can get only through years of experience.

I think this course is one of the best course which teaches you how can you proceed your machine learning project in various situations and what measures to take while building a ML project. Hats off and thank you Andrew sir. Thank you coursera.