Going beyond the technical details, this part of the course goes into the high level view on how to direct your efforts in a ML project. Really enjoyable and useful. Thanks for making this available!

This course presented repeated some of the material from previous courses, had limited challenging material and no programming. It was much too easy for anyone who had already completed the first two courses and it should not have been a standalone course but rather could easily have been part of another course.

Time wasting, all could be summarized in 30 mins video at the end of the previous course

This specialization has increased my knowledge and passion to learn about machine learning.

but that course took me alot as i really hated wasting my time watching aaaaalllll these videos for nothing really really small amount of useful information

Sorry if i was rude, but that's my opinion and that's because i really appreciate coursera contribution in knowledge sharing especially for those who can't afford it (like me)

I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

This course would be immensely helpful for those who have not started on their first machine learning project. However, the insights shared are quite commonsensical and intuitive for those who have already had some minimal experience in machine learning. This course also does not feel as substantial as the other courses in the specialization, though the tips provided are definitely valuable.

This is a must course in the entire specialization. It covers the step by step procedure to approach and solve a problem. The case studies provided are real world problems which are so much helpful.

Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.

This is the knowledge in which we will get from lots of experience only, but the andrew has shared in this course which might help us in future by saving a lot of time through this course experience

It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.

I just felt this course was a bit confusing in the sense that I never really got the chance to apply what I have learned all in this course. For example, for transfer learning, it's much better to have a programming assignment to understand and test as to for what kind of problems is this gonna be necessary and where can I go wrong instead of just a set of questions.

This seriously shouldn't have been a 2 weeks course, Instead, this should have been a 4 weeks course to better understand the intricacies in the learning algorithms and it's diversifications.

I was disappointed.

This course is too elementary and abstract compared to previous two courses. It is more for a folks managing DL/ML projects . I would have expected more hands on coding experience for much deeper concepts in this DL course rather some very elementary theoretical discussion on how to Manage ML Projects.

Awesome course as always. The course teaches real world practical aspects of how to get started and navigate in the real world projects. The guidelines are actual learnings from years of experience.

I recommend this course. This will be a bit of theoretical which is good. It will talk about real world scenarios over the errors which is what we deal in day-to-day life and how to deal with it.

After doing this course I was able to optimize the performance of my model. Prof Ng does a fantastic work explaining the intricacies involved in rather simpler words and with very lucid examples. The exercises are very well made and let you deploy the concepts learnt. Overall it's an amazing course and a must do for those who have some foundation of deep learning and want to delve deeper in.

A lot of concrete examples, including those in the lectures and in the tests. Gained some thoughts on how to manage a ML project. Thanks Andrew and deeplearning.ai for providing such a great course.

more practice!

There were many video editing errors and grammatical errors in the writing in this course. I felt an increased cognitive load and more frequent distractions because of this. I would be happy to volunteer some time to help with this if you contact me.

The pacing of this course felt strange; week 1 was very short and the material felt less important that week 2.

I would have really enjoyed a programming assignment with transfer learning. Since we already built a cat identifier, making a monkey identifier, or flower identifier would have been lovely and fairly easy, I think.

I'm glad I spent some time on the "Flight simulator" assignments in this course. It's the first time in the specialization when I actually found the quiz questions challenging, and that's a welcome change. However, I didn't learn too much from the lectures. They were too repetitive, either repeating themselves or the material from the previous course. One or two videos could also do with better editing work: I could hear Andrew making a soundcheck, and there's a 30sec segment that's played twice in a row. Overall, it's probably worth doing this course, given that it requires very little time, and the assignments are useful.

I found the course rather confusing than helpful. One of the key issues with video-only courses is lack of interaction of the user with the material. In previous Andrew's ML courses, this issue was cunningly tackled with "wake-up" multiple choice mini-quizzes. Such techniques would help the course a lot.

The questions in the exam were poorly phrased and full of typos; some had numerical issues (percentage of errors in the dev set did not sum up). Some of the answers seemed to contradict with the material as I remembered it from the course: f.e., the question on whether to get more foggy images to improve the model performance should have been answered with "augmentation is fine as long as it looks fine to the human eye". This contradicts to Andrew's remarks in the course video "Addressing data mismatch" video -> Artificial data synthesis. Are you sure we would not introduce a bias by adding artificial fog to frontal camera images?

The 3rd course is more art than science. There is a lot of breadth, but we cover each topic in passing. Therefore, from a student perspective, I find that the concepts are not cemented and it is entirely possible that I forget them once I move on to the next course.

The second issue I find with the course is that there are no programming assignments. Programming assignments. Programming assignments are key to understanding such complex topics and getting the idea cemented. It would have been much better, if we could cover each topic such as data-mismatch, comparison to human level performance, etc via assignments.

There is not much content in this course to be offered as a full course of its own. Andrew just repeats the same material over and over again; you will find this true if you have already completed the previous courses of the Specialization.

To set the context, I have a PhD in Computer Engineering from the University of Texas at Austin. I am a working professional (13+ years), but just getting into the field of ML and AI. Apologies for flashing this preamble for every course that I review on coursera.

This course is the third in the deeplearning.ai series offered by Dr. Andrew Ng. This is a relatively short course as compared to the other courses in this series. However, there are quite a few videos to watch and learn from. This course is really a series of practical advice, strategies and analysis techniques that are an indispensable part of the ML/DL toolbox of a practitioner. The techniques are presented through a series of examples and Dr. Ng helps beat the "practical theory" into the student very well.

I was at first disappointed that there were no programming proects. However, the "flight simulator" quizzes were quite challenging and made me think -- thereby, more than made up for the absense of projects. This course is a critical part of the entire series and it is best understood when taken as a part of the sequence.

Thanks Dr. Ng and teaching assistants. This is a fantastic course. Thanks, coursera.

this was definitely a useful course, as it addressed the 'art' of machine learning.

For me, the mathematics and writing code is easy - that's the science; however, it is equally important to have heuristics for deciding what sort of learning algorithm(s) to try, and how to start, and how to iterate.

That being said, some of the terminology is peculiar - satisficing, for example, is that even a real word?.

In the software requirements engineering field, we'd call that performance requirements ( for run-time speed), or perhaps non-functional requirements( memory usage), depending on the metric.

Also, in the second week, there was a discussion of error priorities for the autonomous vehicle example and quiz where a safety-critical requirement was not taken into consideration at all.

Spoiler Alert: If I am building the AI and control systems for a vehicle ( autonomous or otherwise), , that has to work in all weather conditions, no matter how hard it might be to get the necessary training data. Qualifying the answer with 'all other things being equal' never applies to safety-critical systems.

This is something you won't see in every machine learning courses. Well, Course 1 and Course 2 are also good. Andrew definitely has a thing to explain complicated stuff in the easy way, e.g. the part where he explained how Adam works in Course 2 is truly amazing.

But this course is really different. It appears to have no technical details and I can see some people may consider this course worthless or make disparaging remarks. But based on my personal experience, what he taught in this class is really important and kind of shapes the way one needs to think about how to tackle a machine learning project from the start. As researchers and engineers, it's easy for us to delve into the technical details and algorithm approaches, papers too early when the overall direction is not too clear yet. I feel very grateful for what Andrew did by sharing his knowledge to all of us.

PS: I believe the content taught in Course 3 is also similar to Andrew's recent NIPS tutorial: Nuts and Bolts of Building Applications using Deep Learning. (https://nips.cc/Conferences/2016/Schedule?showEvent=6203)

First of all, I thank Professor Andrew Ng for offering this high quality "Deep Learning" specialization. This specialization helped me overall to gain a solid fundamentals and strong intuition about building blocks of Neural Networks. I'm looking forward to have a next level course on top of this track. Thanks again, Sir!

I strongly recommend this specialization for anyone who wish get their hands dirty and wants to understand what really happens under the hood of Neural networks with some curiosity.

Some of the key factors that differentiate this specialization from other specialization course:

1. Concepts are laid from ground up (i.e you to got to build models using basic numpy/pandas/python and then all the way up using tensorflow and keras etc)

2. Programming Assignments at end of each week on every course.

3. Reference to influential research papers on each topics and guidance provided to study those articles.

4. Motivation talks from few great leaders and scientist from Deep Learning field/community.