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!

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 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.

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.

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

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)

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.

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.

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!

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.

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.

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.

I really liked to learn about the actual problems faced in a project and the ways to tackle them more or less systematically. I also understood the challenges and open questions in case of dead ends. The two quizzes really can help me answer a typical deep learning job interview. I definitely feel prepared for a job in deep learning industry. Finally, the interviews with Andrej ( I have read his blogs but never got to see a video/picture) and Russ were thrilling and keeps me motivating to not approach deep learning as a subject solved but an evolving research area. It also tells me to revisit some of the concepts like autoencoders, RBMs which are normally not dealt in normal deep learning class. Once again, I want to thank Prof Andrew for his simple, elegant and thought provoking lectures which are not only specific but also fulfilling. It is extremely interesting to do his course just like watching a favorite movie/ series. Thank you Coursera team !

This course is radically different from the first two of the specialization. While before we were dealing with the theoretical basis for how learning works and ways to optimize the performance of the computer, this one is more like a stream of tips, cautionary tales, and hacks in order to optimize the performance of the human. Personally, I found the material to be very educational and engaging, with many "aha" moments when the instructor makes you see the "obvious" solution for a problem that just seconds ago seemed unsolvable.

The assignments (the "flight simulator") are incredibly useful and make you think profoundly and systematically on the problems. I found that the questions would typically prompt even more questions in my head and make me consider many options to tackle a particular problem.

After seven days learning, I finally finished the three course of this specilization. I've gotten much more than I've expected at the beginning. Not only deeply understand how the neural network works, but also how to build deep neural network and how to train it efficiently. Now I know how to start to build a machine learning project and solve the specific problems from data preparation to model training and I know how to quickly get my network works through transfer learning and fine-tuning, etc. By watching the interview videos I got a lot about the future of AI and I deeply know what I am really interested in now. I really appreciate what Prof. Andrew and TAs have done to make this series available from all around the world and I really too impatient to wait to learn the next two course.

This is probably the most important course in the specialization. It's very easy now-a-days to create Neural Networks and get a grasp of how they work due to high-level frameworks (keras, scikit, tflearn, etc) and abundance of literature and videos, respectively. The thing that is lacking from most resources that I have encountered on learning Deep Learning and Neural Nets is how to optimize and approach problems. I have in the past build some complex Neural Networks, but would hit road blocks that would ruin productivity for I didn't know how to approach problems correctly, and didn't know what knobs to turn to improve performance of my program. This course teaches techniques that I find extremely useful for my previous problems in Machine Learning.

Very useful knowledge, super interesting material and prof. Ng is an awesome teacher as always. The simulating approach for the quiz is great! However the "simulation" questions and answers should be carefully reviewed. Sometimes the "right" answer is difficult to choose because of an ambiguity or a little detail that does not quite match the lectures and two answers seem to have some of the right element OR no answer seems to be perfectly right. Going thru the forums, you will find plenty of comments like this to figure out which questions to tune. Some are right and some are due to the student genuinely making a mistake. Perhaps looking at the error rate on each question will also help seeing which one are abnormally incorrectly answered.

Loved the course because the insights shared by Andrew Ng are clearly coming from real-world industry experience. Besides the content of the video lectures, which are a must-see for every ML practitioner, I particularly liked the "flight simulator"-style assignments.

Although the content is of very high quality, I noted that there a couple of mistakes in the assignment texts, unfortunately sometimes even in the options of multiple-choice questions, which make it unnecessarily hard to guess what the option actually means. In one case (assignment 2, question 10) I even think the "correct" answer's text is contradictory to what Andrew says in the lecture. I feel that half an hour of proof-reading could have taken care of these mistakes.

Previously, I have taken online classes before in Machine Learning by going the cheap route (Udemy, blogs, youtube) and you get what you pay for. Andrew Ng explains it the most thorough, easiest, and simplest way possible. Presentation material is very understandable. Great class for new machine learning learners. Highly recommend it. The only downside is that the programming exercises are little too easy in my opinion. I feel like the best way to get your hands dirty is to do actual projects (do your own projects). These lectures are good for intuition and background of different types of Neural Network architectures. Other than that, Great material. Thanks Andrew!