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

48,973 ratings

About the Course

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews


Jul 1, 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!).


Dec 1, 2020

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

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4526 - 4550 of 5,585 Reviews for Structuring Machine Learning Projects

By John R

Aug 5, 2019

The quizzes were a little annoying to get through, as it is not much about deduction or reasoning, instead it's about learning the advice or rules mentioned in the videos. I think an actual implementation of a learning project and applying the error analysis, transfer learning, etc, would be more beneficial for the student.

By debraj t

May 10, 2018

Gave me a broader and more strategic perspective on how to structure and run a Machine Learning project.

I just felt this course came too early in the learning process. It would have far more relevant and useful had it been a more downstream course.

This does not take away from the fact that the content is very relevant

By Kang C

Oct 23, 2022

Material is great. Quizes could be better imo. Sometimes I get the questions wrong not because I don't understand the materials but because of the choice of words in a question. Also would be better if there are actual real world case studies instead of just going over the concepts of how to structure a ML project.

By Uğur A K

Nov 15, 2019

This was a good course because it "kind of" prepares us to real world projects and we think about what to do when different problems arise. I would also really like if this course included a section on how to create datasets from images, sounds etc. and prepares us for the "boring" parts of machine learning as well.

By Jacob B

Mar 3, 2022

This course provided some interesting strategies and advice on how to start and struture machine learning projects. My only complaint is that this course should be placed at the end of the specialization. I felt like I wanted to know more about deep learning models before I learned how to strategize on deployment.

By Zahin A

Jun 29, 2020

Was extremely helping in providing ideas on how to start and work on machine learning projects. Provided clear and well thought out ideas on how to make the most use of time and data. A small improvement can be made to the course by dividing some of the contents of the course to another week for better structuring.


Jun 17, 2020

Course is great. All concepts are explained very meticulously. Lots of respect for Andrew NG. Just a small suggest please don't give more examples on cat classification. Autonomous driving case study was good, speech recognition examples are good. Please give more realistic examples, that can be used in interviews.

By Ranjan D

Jul 17, 2019

Great explanation on how to structure your machine learning projects like distributing data among train & dev/test set then what to do for each type of errors to continues to transfer learning, Multi task learning, End-to-End Deep learning. It has been a fantastic journey learning about these different techniques.

By Katherine T

Jan 8, 2019

There were definitely useful pieces of information in here, but I think it could have been condensed and delivered as part of the previous course. I liked the flight simulator quiz approach. Sometimes the wording of the questions was tricky and that may be causing people to get stuck even if they know the material.

By Nicolás A

Oct 14, 2017

-You should edit better some videos, in some parts Andrew repeated what he said, or there were long silences, or also what he was writing wasn't in tune with what he was saying.

-I'm not sure if the topics covered here justify a whole course. Maybe the insights shared here could have been inside some other lecture.

By Matt P

Feb 15, 2019

The flight simulators' results were not consistent with the advice provided in the lectures. I'd suggest being either less black and white in the simulators' answer responses, or, being more polarised (more black and white) in the advice provided in the lectures. Otherwise, this is a 5 star course. Many thanks!

By Fritz L

Sep 23, 2018

I liked the course but it contained quite a few glitches which could be easily removed to improve the overall experience. E.g., once Prof. Ng makes a long pause and says "test". Sometimes the same ending is placed twice or in the final "Heros of Deeplearning" video Prof. Ng seems to ask the same question twice.

By Jingchen F

Jul 7, 2018

this course is pretty different from other courses in this specialization. It gives high-level knowledge of machine learning instead of implementation details. The course content is useful but it seems a little boring to me because I can't do any fancy, real machine learning projects as exercises in this course

By Edgar L V

Aug 5, 2019

The quizzes were actually a great idea. The content is definitely useful, as I've had similar difficulties in my company. I felt the videos took much more time than they should, though. A lot of the content could have been resumed in shorter videos. It was the first time I actually had to accelerate the speed.

By Sebastiaan v E

Nov 17, 2017

Good materials.

This course was really short though. It seems to be a bit artificial to make a "specialization" out of these courses, where they could easily also fit into 1 longer course. Fortunately the dates you can start the courses are flexible enough that you don't need to wait (too long) between courses.

By andrew w

Jan 26, 2021

Excellent information about how to diagnose errors during machine learning and complete projects well. I would have liked a small coding aspect to see how certain concepts (eg. train-dev set, transfer learning etc. are implemented), even some very basic examples would have helped. Overall still a great course

By Rosmiyana

Apr 13, 2020

Good course to get started with Machine Learning, the introduction video could have used simpler languages though as many of the jargon might not be familiar to newbies (therefore scare us off!!) and they are really not necessary prerequisites to the course. I enjoyed the quizzes as they are real and useful.

By Alexandru S

Sep 8, 2017

Very interesting material covered - not too many courses have this kind of information.

A little too short and very no practical assignments (only quizes). It would be very useful (although I agree quite time consuming to prepare) to have some programming assignments that deal with the topics in the curse.

By Jasper

Apr 3, 2020

Good general introduction to analyzing errors and avoiding common mistakes in machine learning projects and some info on transfer learning and multitask learning. Could've used references for further reading. It should emphasize exploratory data analysis and an ethics review as the start of any project.

By Ernst H

Jul 9, 2019

4 stars for a very good course that should be improved. Course is still good, but it is not as polished as the first courses in this series. I rated those with 4 stars, too. There are mistakes in the quiz names, grammatical errors in quiz questions, etc. Never-the-less, it is the best of its kind.

By Karim A S

Mar 17, 2021

Good course helped a lot to gain insights into the problems of machine learning but I would say more exercises are needed even if these guided exercises are good.

maybe add an exercise where you can simulate a fake NN and get the result and then choose what to do to get a feel of what you should do.

By MIchael

Nov 19, 2017

Interesting insights.

The insights could be visually structured a bit better so that I can also check them after the course as a reminder.

Often recommendations like if then could be put in processes or cheat sheets

overall: very valuable course regarding the insights and encouraging style of Andrew Ng


Jun 22, 2020

Well structured course, Andrew always never lets down your expectation, the explanations were very clean with the best appropriate examples to suit the explanation. Being more of theoretical, the task of giving us the correct intuition is really well handled by the way the lectures are structured.

By Kharuk I

Jul 20, 2020

Instead of clearly and concisely formulating some of the ideas (like F1 formula for the metric), they are discussed as if they were hard to understand. This makes the understanding harder and is rather annoying. The assignments are useful - they make sure you understood the material the material.

By Burag C

Apr 10, 2018

This was a good intuition course. I learned a lot and loved the content. However, I am afraid the information here needs to be repeated many times to make it a habit (as part of programming exercises). That's why I am giving it a 4-star. I feel like this could have been part of the last course.