A very well systematic approach explained, to structure ML projects.Can be grasped and implemented by anyone, let it be a beginner or some expert.Really liked the idea of case study in quiz. (Wait ! How about extending this idea into some coding exercise ? Would be some real fun !)

There is no coding in this course, but you learn a lot of how to design a Deep Learning Study. I learned a lot about the distribution of Training/Dev/Test sets and how to diagnose problems when a neural network is not performing as well as anticipated or if it is performing well.

Outstanding course. Many of the points made in this course mirror the hard earned knowledge I gained back when I worked on Dynamic Rank search engine focused neural networks.

This may end up being my favourite of the 5 courses but let's see if the last two have more math first. :)

This part of the specialization is short but it includes a lot of valuable information. Many of the tips are quite basic engineering best practices which most engineers should find natural, but some are very specific to deep learning and these are particularly useful to newcomers.

This is truly some information you'll never get in a standard class setting; this is more similar to compiling years of ML experience into short packets of advice that will guide your decisions for years to come. Extremely helpful, and recommended for all deep learning engineers.

This course is really what any PhD would need to conduct his research in more time saving and efficient manner. It would be great if coding was accompanied (even if only running and watching results) to touch all aspects of analysis and suggested improvements could be visualized.

Great course. I had a couple of ML courses at University, but nobody ever explained those concepts: orthogonalization, the data mismatch problem and what to do with it, different versions of human-level performance, end-to-end learning pros cons (everyone just talks about pros).

To Andrew and team (mentors and organizers), I am glad I opted for this course! You guys give such great insight on approaching and solving a Deep Learning problem, I don't think I would have ever found a better introductory course on Neural Nets. Thank you so much, everyone!!!!

One of the most important course in this series . This course actually helps you visualize the problems and standstills you might face when you are working on a model in real life. It also talks about practical solutions to improve your model that are valuable in the tech world.

An unique course. I don't think such a course is offered by any MOOC. I would suggest every DL enthusiast to take this course.

The programming assignments are very challenging. It forces us to think abstractly to find solutions encountered during real life Deep Learning problems.

Despite this course is labeled as basic level, it contiains very useful information related to strategy of developing ML projects. And use cases prepared by prof. Ng and his team is what you will get only by practice. It really helpful to structure what was learned by this day.

A great insight into how to improve the performance of the deep learning system without having to actually spend long hours/days and working on real project. Learnt a lot in improving the model's performance and where to look for the errors and how to invest time in debugging.

I totally agree with your flight simulator analogy. This really helps me to learn your experience in practising machine learning knowledge; which otherwise I need to spend many years of doing "try and error"

Thank you very much for your kind sharing of your practical experience

As always, very well structured material considering the nature of the content and trying to make it understandable and make sense. I also appreciate that it is rooted in real-life experience which serves to make me pay really close attention to everything that is being said.

This course teaches the trade off between Bias, Variance, Data Mismatch . You will learn how to split data and how to evaluate your model. It also covers error analysis systematically. It gives many examples of transfer learning, multi-task learning, and end-to-end learning.

Very refreshing to learn about also the more practical aspects of machine learning project like organising, structuring and executing the projects. The course definitely gives me more ideas now on what to do when starting a project and what to look into when facing problems.

Very useful guidelines for approaching projects! This topic is rarely addressed in comparison to the discussion of modeling techniques, however, in the real-world application, the trade-offs on where to start and how to proceed are just as important as the model themselves.

Great course!!! The best online course I have ever taken! I enjoyed almost every day I participated in that course, really an educational treasure! It is so comprehensive and detailed at the same time. Due to the good presentation of the topics it was really understandable.

I have learned tremendous things about machine learning projects which I feel if I have not learned and started any machine learning project than it would have taken me many months or years to complete. Now i know how I could complete my project efficiently and effectively.

I can only say that I am amazed how I much learned by just watching the few videos of this course. It is so short but still contains plenty of new information. It also helped me at work by giving me a deeper understanding of how to approach various problems. Awesome course!

This is a very good course that which should be helpful for project managers/leaders that tells on which direction should the machine learning team to go as it gives techniques and intuitions on how to decide on which direction should a certain machine learning project go.

This is an excellent course and I was able to understand with clear explanation, example and practice cases on how to improve the Deep Learning workflow in order to make the right decisions on what direction the team need to take to improve the DL model. Highly recommended

I have decided to reserve 5 star ranking exclusively for Professor Andrew Ng. I did a course which was learning to learn which was quite good. A course by Prof. Andrew Ng titling "learning how to teach" would do tremendously in propelling the world of data science forward.

I loved this course. Although there wasn't any exercise other that quiz, this was pretty interesting and gave me a lot of ideas to try. I was wondering about how transfer learning would work before this, and now I know how it works! I am looking forward to the next course.

This course confirms the capacity of Andrew Ng to teach complex topics in a simple way. The course is full of advices and trick to structure and to success with machine learning projects. Suggested for people that already took courses on machine learning and deep learning.