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

48,950 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


Nov 22, 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.


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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576 - 600 of 5,583 Reviews for Structuring Machine Learning Projects

By Wilem

Nov 24, 2019

Really interesting!

We used to be concerned about unbalanced train/dev/test, and with this course I realised this are not the main problems for achieving performance in ML

A master class.

Thanks Andrew!

By William G

Jul 14, 2019

not as technical as the first 2 courses in this specialization (and the next 2 for that matter), but it is still a well rounded course and highly recommend to do all the courses in this specialization!

By Karthik V

Sep 11, 2018

Extremely interesting and useful practical advice that can help make significant difference when thinking about how to identify and correct problems. The quizzes were fantastic and made me think a lot.

By Ernesto S

Dec 4, 2017

Excellent material. I would say this is the most important course of this specialization. Knowing how to approach a certain problem can indeed save us a lot of time and help us avoid a lot of mistakes.

By Wonjin K

Oct 1, 2017

This course gives great intuitions to develop deep learning model and how to go with deep learning project. I was really impressed and felt like I gain a real experiences without working at industries.

By Erick D

Aug 22, 2020

Excellent start for digging into topics that are not taught nowhere else. The author books 'Machine Learning Yearning' is a great next read that goes deeper in some of the aspects, really recommended.

By Maha A

Nov 30, 2021

It is good course that gives the practical insights about implementing the machine learning problem. However, it is better to have some coding exercises to be able to grasp the idea more efficiently.

By knguyen

Aug 20, 2021

Very helpful tips for navigating possible problems that would likely occur while building/training a model. The "pilot-training" exercieses, that mimick real-life problems / projects, are excellent !

By Frédéric G

Sep 29, 2020

Excellent. Just one remark: sometimes I do not understand quite well the english sens of the sentence. But in overall the course is well structured and I've learned quite a few things in ML Strategy.

By charles

Jun 16, 2020

Useful to know what are the steps that should be taken after obtaining results. Tho there isn't much information regarding making machine learning projects here (ie. there isn't any hands on project)

By Antony W

Jul 17, 2019

I am glad I took this class. There are a lot of things think about with respect to structuring your M/L project. Fortunately, it is not as mysterious as people often claim...but it is very nuanced.

By Yong H P

Jul 25, 2018

Very important and valuable intuitions about DNN training/optimization. It's full of really practical information while implementing my own models.

DNN을 실제 적용할때 반드시 이해하고 적용해야 할 실질적 내용들로 구성된 멋진 코스 입니다!

By 翁嘉进

Apr 7, 2018

It is a special lesson that guide me to think how to build a good model for ML. There is no doubt that Andrew ng taught his project experience without exception and hope that we can benefit from it.

By Amit M

Mar 8, 2021

Excellent course to understand the various nuances of structuring these kinds of "projects". Though if the content were presented as parts of programming assignments could have been easier to grasp.

By Muhammad A

Sep 14, 2020

Andrew Ng, awesome teaching technique and well-designed course content make it easier for deep learning beginner to learn how to structure your machine project smoothly and do not lost in a process.

By Bijaya B

Jul 7, 2020

The course was very insightful on how to tweak and evaluate and measure the performance of your model. I loved the course very very much. Hope to see more courses from and Andrew.

By Sai S B

May 7, 2020

This course gave some very useful tips on how to start with a Machine learning project when I was struggling to do so. It also gave useful information about error analysis and data set distribution.

By Х. А Р

Apr 3, 2020

I think this is the most useful course in the Specialization. Andrew reveals secrets about details which can speed up working in Deep Learning. It will help to avoid marking time in future projects!

By 杜谦

Feb 25, 2020

Generally, the course is great. This is a short course and could be combined with other courses in this series. Also, some knowledge such as data splitting has been introduced in the courses before.

By Amey N

Nov 12, 2019

The course gives a sound intuition and insight into the parameters to be considered and the crucial thought process involved in making the decisions for improving the performance of neural networks.

By Anshu S

Jun 21, 2019

Really a good course with mostly the theoretical knowledge on some aspects to reuse your model as well as some error analysis. Thoroughly taught with lots of real-life examples, thanks to Andrew Ng.

By Young L

Nov 29, 2017

It's a great course! This course gave me a lot of new perspectives in constructing a machine learning project. Especially, the discussion of data distribution in the train/dev/test set is fantastic.

By Balachandra J

Apr 25, 2021

Insights distilled from long experience, explained with simple examples. Probably like explaining quantum mechanics to someone who didn't arrive at the conclusion through rigorous first principles!

By Federico R

Jun 6, 2020

I really liked this course, such as I liked the rest of the courses in this Specialization. I honestly appreciate that this knowledge is shared, accessible, and made extremelly intuitive and clear.

By Naveen N C

May 11, 2020

Really a good course and got an insight into how to structure a machine learning project and some useful techniques for deep learning, such as transfer learning, multi-task, and end-to-end learning