I enjoy Andrew's approach to managing AI projects. He hits on very real issues we encounter when young, enthusiastic scientists want to solve problems with ML, but get lost in the numbers and multitude of possible next steps to take top improve the quality of a model.

Es un curso extraordinario, entrega muchos detalles que hacen ganar destrezas a la hora de entrenar nuestros modelos y el profesor Andrew Ng muestra mucho información sobre su experiencia. Creo que hace un gran trabajo y me siento afortunado de haber pddido hacerlo.

This course is a little bit hard for me, because I never heard these concept before. But through this course, I got a lot of new ideas to start my own machine learning project, and use these knowledge in practice. Thanks for the nice course and I will keep learning!

Although the topics seem easy, they are very important. It contains important information on how you should spend your money, time and energy most efficiently. Important tips to get started 1-0 ahead when building a machine learning system. Be sure to check it out.

Esta fase del curso es de gran importancia para saber como encarar un proyecto de deep learning con sus mejores prácticas. El proceso iterativo de desarrollo es bien explicado y te permite construir la base de cualquier tarea de deep learning que vayas a emprender.

Though we learn different machine learning models to train, we might miss the logical key practices and struggle and waste lot of time while training to achieve better results. This course teaches those important practices to efficiently implement the ML projects.

Very helpful course. This course is very helpful that I got to know the things beyond the technical details of the neural network. About selecting test and dev set distribution, transfer learning, error analysis , end to end deep learning etc. Thank you Coursera...

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.

According to me, It is a very good course. In this course, they have taught about what can be the best practice to handles errors in the projects. They have taken different scenarios and gave us what can be the best choice that you can take to handle the problem.

Once a person has a knowledge of how to become a developer in the prevoius courses, this course gives you the knowledge to escalate from developer to a project architect. It teaches crucial techniques required to guide the training model to produce better result.

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

This is an exceptional course which helps tremendously in deciding the strategy for Deep Learning projects. A course which is based on practical challenges we face during implementation of real ML problems and projects. Thank you Andrew Ng, this is very helpful.

This was a much needed course after the 2nd one in this specialization! This course provides a clear and essential picture that is needed during development stage as well as shows the scopes of applying different techniques/blocks that were taught in course two.

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.

Very well structured (pun intended) and informative course! Andrew makes a fantastic job transfering his knowledge to his students. I gained very much insight in general for prioritizing different tasks in machine learning projects and planing the future steps.

This class, although short, was very interesting and serves as a useful guide to aid in building a deep learning model from scratch. The quizzes are really interesting as well as it makes you think about a step-by-step process in your own model implementation.

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!

At first, I don't think this is a too exciting course for me. But it turns out really helpful to learn more about how to debug your ML model and make it better. It introduces many ways and approaches to debug a ML model and the solution to the problems as well

It is vital before diving in advanced concepts in ML or DL to learn about how to structure ML projects. This course paves the way to this fundamental hot topic. As usual, Andrew makes it easy and illuminated to clearly understand and develop your skill in DL.