I have learnt algorithms theory of machine learning and how to use them into data mining. However, I’m not good at ML strategy, so I can’t build a very well model. Through this course, I know many methods to improve models which I build before. Thank you very much!

Amazing Course ... different structuring strategies involving Orthogonalization, Single number evaluation metric, Carrying out error analysis, and how Cleaning up incorrectly labelled data including Transfer Learning are Beautifully explained by Andrew Ng...KUdos

The best part of this course was the "ML Flight Simulator" questionnaires (peacotopia, auto-driving). These real life examples and grading on that is the best thing. It helps you learn and interpret the concepts much nicer. I wish there were more of such examples.

This course has been really insightful into how we should work with machine learning projects. True, that most of the ideas discussed here are not covered in normal university curriculum. Thanks a lot to Professor Ng for coming out with this really helpful course.

I feel this is an extremely important course for all aspiring deep learning practitioners: being good is not only about knowing the algorithms and architectures, but also how to best manage your time and choose the most promising avenues to explore. A rare course

Shows methods for improving performance of deep learning setups in a time efficient manner. I like that deciding what to try next for improving a deep learning setup's performance is a recurring topic in Andrew Ng's courses. Knowing this can save a lot of time.

A fantastic course. I believe the strategies presented are practical and powerful in actually developing a deep learning system. I very much look forward to encountering those first problems and using these strategies to guide my model development. Well done!!

Great course covering topics that are not seen anywhere esle and are probably highly udnerestimated. The graded simulations are a very good idea that could be used in other courses as well. Professor Ng excellent performance is no surprise for anyone having fol

This strategic thinking for planning ahead will absolutely save tons of times of ML developers when building a real-world ML system. This course gave me a lot of thinking points, even without programming assignment. Thanks professor Ng, Mentors, and classmates!

Excellent course on how to analyze errors of machine learning applications. This material provides good ways to improve the speed of iteration over any machine learning project.

This class is unique as professors rarely provide this kind of insights. Thank you!

Worth the time and effort. Although this course did not contain the programming aspects, but it was helpful nevertheless. This course actually taught me how to properly go about my machine learning project and how to troubleshoot if I encounter some problems.

This course give me direction to structure my project in better way. Content of this course was really awesome and most amazing part was the flight simulator for machine learning. Thank you Andrew Ng sir for beautifully presenting the idea, thank you so much.

Short course but i think this is the most important course out of the 3 as it is more applied. Everything in this course is new to me... , had to read the discussion for help on the quiz. Hope to appreciate what i learn after i start my deep learning project!

Knowing the algorithms alone doesn't help much in developing ML applications. We should be able to tackle any problem and drive our project towards the intended goal.This course provides some handy tips and tactics for the same.Well taught as usual. Cheers!!!

This course is very useful. The 'Simulator' is very cool. After finishing the homework, I have a better understanding on how do deal with a real project. I'm trying to solve a problem in my work, I think this skills mentioned in the course will help me a lot.

Un curso corto con mucha información, pero muy muy instrucivo de cómo abordar los proyectos de deep learning o redes neuronales, se te enseña desde el análisis del error hasta la transferencia de conocimiento, lo cual es bastante interesante.

Muchas gracias!!

It's really nice to have Andrew share his practical knowledge and experience. Paying careful attention to data distributions and doing ErrorAnalysis to learn where to focus your efforts are valuable insights. Thanks for making us all better DL practitioners.

This course gives you a good understanding of how to approach deep learning projects and machine learning problems in general. After this course you should feel more comfortable understanding how to structure your projects and better optimize your time use.

Andrew provided lots of information in a two-week period due to this the course feels more dense than the previous two. The quiz has also been more challenging. Overall though, it is still top notch teaching from the best. Looking forward to Course 4 and 5.

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.

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.

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.

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.