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Learner Reviews & Feedback for Structuring Machine Learning Projects by DeepLearning.AI

44,224 ratings
4,987 reviews

About the Course

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization....

Top reviews


Jul 02, 2020

While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).


Nov 23, 2017

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.

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26 - 50 of 4,936 Reviews for Structuring Machine Learning Projects

By Md. Z M

Jun 25, 2020

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.

By Victoria D

Nov 28, 2019

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.

By Shibhikkiran D

Jul 08, 2019

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.

By Kumaraguru S

Nov 20, 2017

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 !

By Zeyad O

Apr 15, 2020

I'm Zeyad, an undergraduate of Computer Engineering at Alexandria University in Egypt.

Taking this course really helped me to learn and study this field and also to implement it. It helped me advance in my knowledge. This course helped me defining Deep Learning field, understanding how Deep Learning could potentially impact our business and industry to write a thought leadership piece regarding use cases and industry potential of Machine Learning.

This specialization helped me identifying which aspects of Deep Learning field seem most important and relevant to us, apparently they were all important to us. Walking away with a strong foundation in where Deep Learning is going, what it does, and how to prepare for it.

Deep Learning specialization helped me achieving a good learning and knowledge about that field.

Thank you so much for offering such wonderful piece of art.

Best Regards,


By Nkululeko N

Apr 15, 2020

Failure on a beginner level quizzes it's very irritating, more specially to me. I don't regret seen myself have to re-do some quizzes for every week, probably that's because English is not my first language, or how can I put mother's tongue. I believe it's an indication of our weaknesses and if we face them we can grow to prosper, not that I am trying to be a life philosopher. Questions on this course are made in such a way to test if you really understood what the instructor has taught you. I love Andrew's ways of teaching, I just wish he was my electronics lecturer. I feel like I could have understood some of fuzzy concepts that I battled with very easily. The concept were given in such a structured way and I was very excited in many of these teaching and insights regarding machine learning approaches as a machine learning engineer.

By David M

Sep 01, 2017

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.

By XiaoLong L

Aug 15, 2017

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.

By samson s

Dec 09, 2017

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.

By Louis-Marius G

Oct 20, 2017

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.

By Michael K

Aug 14, 2017

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.

By Francis S

Aug 26, 2019

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!

By Chou C C

Dec 10, 2017

In this course, I learned a lot about how to make right decisions when facing different problems in machine learning tasks. It helps me to review the decisions I made in the past, and also shows me a more systematic way to think about what to do next. I strongly recommend everyone interested in ML to take this course.

The only thing I'm not so satisfied with is that some questions in the quiz are quite confusing. Maybe they just have wording issue, but these questions and their corresponding answers do confuse a lot of people. I think maybe TA could take some time to address these problems in the discussion forum and help us learn even better.

By Ernest S

Oct 29, 2017

Another excellent course made by Andrew Ng. It is another perfect example of how to prepare good learning materials.

This course does not in fact expect you to write the code. Teacher is aiming not to offer you his abilities to make working system. He is offering you his deep insight and experience in making systems better and better to the point in which they meet expectactions. He discusses how to address issues you may encounter in systematic manner and where put your resources to use them in most efficient way.

If you are building machine learning models I am sure that this course pays off and can spare you many mistakes you could make.

By José A

Nov 06, 2017

This is a passive course. Don't let the 2-week course set you off. The videos in here are really insightful. They give you some of the experience that Andrew has seen throughout the years.

They will provide you with the right way on how to split the data sets, how to handle when the train, dev & test sets come from different distributions; advantages of orthogonalization; The avoidable bias, the satisfying and optimizing metrics.

By investing in this course, this will save you tons and tons of hours of work by understanding some key concepts that you will need for an effective Machine Learning problem.

By Ali A A

Sep 25, 2018

An amazing course indeed. A bit "dull" to some due to the lack of programming assignments, but extremely beneficial and insightful to anyone seriously considering to tackle an ML project. You have to appreciate the fact that while what this course covers may sometimes seem like "common sense", it is still reassuring and comforting to know that these concepts and principles are what the likes of Prof. Andrew Ng go by when they embark on an ML project.

To all who are working on making this platform what it is, I'm very confident that it is not an easy thing at all, so thank you so much.

By Daniel C

Feb 01, 2018

This course provides valuable practical advice on overcoming common obstacles in machine learning and deep learning projects. Some people might dismiss these advice as "common sense", and they would be wrong! Common sense isn't so common most of the time. In other words, there are many advice and suggestions this course offers that I hadn't thought of, but "obvious" once I learned them. Well, I need to hear them, and I'm glad I took this course. BTW , the assignments are essential. You can apply not only what's discussed in the lectures, but also learn new "common sense" methodology.

By Teyim, M P

Feb 15, 2018

The course content is very theoretical but packed with very very applicable information for improving machine learning systems. The use of simulation exercises at the end of each week really goes a long way to compensate for the theoretical nature of the course content by giving learners the ability to think in terms of a real world project and seek ways to make it better. Technically speaking, I found this course more important than most practical courses that are filled with coding exercises without any additional information around making the code perform better. Great content!!!

By Ricardo S

Dec 17, 2017

This is a short high value course. It is especially good for someone who is trying to get into machine learning at a professional level, to avoid the usual pits of project structuring and time management. Highly recommended. It might seem less motivating, because it is perhaps less technical than other courses in the deep learning series, and does not have programming assignments, but in my view it might actually be at least as important as the more technical courses (if not more) in terms of allowing students to deliver machine learning projects in a professional context.

By Srikrishna R

Aug 13, 2018

This course provides insights that you normally wouldn't get reading a book alone. While it does cover the core theories behind structuring of projects, what sets it apart is the truly practical tips and tricks that you could put to use in your project right away. The guidance is actionable and draws from practical experience of stalwarts rather than draw from theory alone. The test & exercise was quite innovative too as it puts you through a real world simulation to help you understand decision pathways you would take based on situational role play. Overall 5 stars!

By David T

Dec 30, 2017

Having talked to someone who is actively working on Neural Network models, some of the insights I learned from the course looked to be helpful to them as well when we talked. I really appreciate the hands-on quizzes as well, as they gave me a chance to critically think through what I had just learned, and apply it to a real-world example. They especially helped when I got things wrong, because then I was able to rethink some assumptions I had made, and solidified my understanding of the material. I hope the next two courses are just as good as the last three!

By Donald R

Sep 23, 2017

This course ia about the practical application of Deep Learning techniques. Andrew Ng's other courses are very theoretical and prepare you with a very strong mathematical foundation for Machine Learning. This course provides practical advice and recommendations for teams building real-world applications of Deep Learning -- advice garnered over many years of work by Professor Ng and others, and, as far as I know, not collected into a single source anywhere else.

I have taken several of Professor Ng's courses. They are all excellent. This may be the best so far.

By Vishal R K

Feb 24, 2019

So far, this has been the most useful course out of this specialization! Sure, the others might offer more technical expertise, but this trains you things that cannot be taught in a class or a lecture. The application oriented case studies are extremely intriguing and challenging to a person whose knowledge might be completely theoretical. This course trains you to think in real life situations of applying a deep learning model, where to cut costs and effort, where to add more, how to optimize your model to surpass even the human level, and go further etc..

By kunal s

Aug 15, 2017

It is one of the awesome courses everyone should join as by investing time for this course you may save your time in future when you are working on real world problems as Andrew has taught his experience where people makes mistakes and how to not repeat it and save your months of time,also he have taught in details about the datasets creation and there use.And also how u can use pre-trained model for other type of dataset. Join and it will make you more curious to dig dipper and also at same time making you better than some of real experts in the industry.

By Benjamin G

Aug 19, 2018

This short course really fills in some gaps in terms of "tricks of the trade"; I think of useful information of this sort as the "force multiplier" whereby some small pieces of advice and insight from a practitioner goes a long way. I checked in a couple of machine leaning books and couldn't find equivalent advice. I particularly liked the point that was made about machine learning and certain ideas becoming obsolete (having previously done a PhD in machine learning) as I had that impression myself and was discussing it with a colleague this very week!