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Learner Reviews & Feedback for Structuring Machine Learning Projects by

32,823 ratings
3,453 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


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.


Mar 19, 2019

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.

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3326 - 3350 of 3,415 Reviews for Structuring Machine Learning Projects

By Leticia L R

Aug 12, 2018

Bit boring.

By Qu S

Nov 18, 2018


By Anatolii B

Oct 01, 2018

some quiz questions are poorly formed, a little disappointing.

By Varun S

Sep 23, 2018

Was expecting more scenarios for real data experience

By Justin M

Dec 03, 2018

As always Dr. Andrew Ng offers great insights into specifics of hot topics (Multi-Task & Transfer Learning) as well as providing unique "studies" as quizzes to complete each week. These quizzes are the primary take-away from the 2 weeks that offer a lot of redundant lecture material. Save some time... just make the 'simulations' the focus of the class then... perhaps use some transfer learning toward a different application in the quiz.

By Liam A

Jun 15, 2019

Kinda boring, but still pretty practical.

By Janet C

Jun 29, 2019

Overview of the machine learning process. No projects or sample code to actually organize the ideas into code.

By Abhijeet M

Jul 07, 2019

Informative but too short

By Nicolas

Jul 09, 2019

not as interesting as the other courses

By Diego P

Jul 12, 2019

Videos are quite long. Good course although a bit heavier than the previous ones

By Tzushuan W

Jun 01, 2019

Wordy and too abstract without hands on experience.

By daniele r

Jul 15, 2019

Good for the numerous hints about practical issues such as different distributions on train/dev/set. Very bad for the lack of hands-on assignments. Good practical advices but no occasion to see them working!


Jul 20, 2019

Learned new things but the course was boring.......

By Andrew W

Aug 05, 2019

Good information about how to structure projects and how to boost performance. Not very hands-on however. Fits in well with the Specialization though as a break before CNN's and sequences.

By Laurence G

Aug 12, 2019

Some interesting information in week 2 where multitask learning, transfer learning and end-to-end vs sequential nets are discussed. The bit on breaking down your errors into classes will also come in handy!

Week 1 was quite repetitive and seemed to be mostly common sense, probably could have cut these videos in half without losing much. Personally I watched most of this at 1.5x speed to avoid falling asleep. First quiz also had some less then conclusive answers - there's a lot of disagreement in the forums! Some issues also with the cutting of the videos, those these are only a minor nuisance overall.

Overall, less impressive then the other courses but still useful knowledge can be obtained here.

From a philosophical standpoint, I especially liked the 2 interviews in this course.

By David B

Aug 19, 2019

No Homework!

By Vincent P

Aug 24, 2019

Was really enthousiastic about the first two courses in the specialization, the third however felt a bit like going back a step in level of advancement.

By Wayne S

Sep 01, 2019

Video lectures tend to be repetitious, and can be confusing.

By Hanbo L

Sep 22, 2019

Good non-technical materials, but short enough to be incorporated into other courses. Some aspects feel subjective. Many typos/minor mistakes in quizzes

By Kanghoon Y

Sep 04, 2019

I got an intuitions from this lectures. But What I want to get from this lecture when I first saw the title, is the method how we can define the activation function at multi-task learning etc. In this video, I got only the overall flows.

By Bjorn E

Sep 09, 2019

Interesting and practical information, but it felt stretched out in an attempt to create a two-week course. With some editing and less repeated information this could be one week that would fit in the prior course.

By Jose P

Sep 30, 2019

Topics are a bit vague, which is fine as the content is interesting and useful nonetheless, but perhaps exposition is too lengthy relative to the amount of content.

By Shivam K S

Oct 01, 2019

Could have been more in depth or could have been added to another course as one extra week

By David R

Oct 01, 2019


Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it. Not sure I would have taken it as a stand-alone course, though.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended

By Sean L

Oct 06, 2019

Bit tedious