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

48,132 ratings
5,522 reviews

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


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.


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!).

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4576 - 4600 of 5,490 Reviews for Structuring Machine Learning Projects

By David A N

Mar 18, 2020

I really appreciate learning about the high level strategies for designing machine learning projects. I only wish there were some programming exercises to put it into practice.

By Søren B

Jan 29, 2018

Based on my own experience and comments on the discussion forums, I get the impression that the quizzes have a couple of errors in them that makes it impossible to achieve 100%.

By Juan Z

Nov 9, 2019

This course is less pratical and theoretical. I don't mean it is not helpful to me. I think this course might be helpful as guideline when I hand on the real project in future

By elie a

Nov 4, 2019

very good course, but I felt like it was lacking one more week of course to get deeper knowledge about how to really get data sets and how to set them up for real applications.

By Gabriel O

May 1, 2022

While the course teach valuable concepts, the material (especially the quizzes) contained many problems, ranging from grammar mistakes, to ambiguous writing, to wrong answers.

By Christian V

Jul 18, 2019

you may think because the course is shorter will be much easier but the videos has a lot of information to process. I am excited to tried this techniques on real applications!

By Ambrose S O O

May 25, 2019

A good course. Provided general high level thinking and reasoning for quick problem solving, data management, multi-tasking, transfer learning, and error reduction techniques.

By Sayantan A

May 22, 2018

Not as exciting as the previous courses, but informative nonetheless. A section for handling imbalanced or skewed datasets would be useful, especially for multi-task learning.

By Aleksi S

Feb 22, 2018

Not as deep into details as the two first courses in the specialisation, but nevertheless I learnt a lot of techniques that I hope will be feasible when I work on AI projects.

By Charles S

Nov 28, 2017

Excellent lectures and notes as always. Great insights and clearly explained. I think we could have used a programming exercise on transfer learning at least in this section.

By Akanksha D

Jan 7, 2018

More coding exercises could be included with much more mathematics background explained. Videos could be made a little shorter. There is redundancy in the some of the videos.

By Juan M

Jan 4, 2018

Good overview of how to structure ML projects with great practical advice. I wish the course had includes a programming lab to help us try out and practice some of the ideas

By Aravindh V

Aug 29, 2020

Good content. The tips and tricks a experienced AI practitioner has was shared. But at least one programing exercise applying all the concepts learnt, would have been great.

By Luis J P M

Jan 12, 2020

In the first quiz, the comments about why an answer is correct are too simple. On the contrary, in the second quiz, the comments are really good and give us better feedback.

By Uddhav D

Jun 2, 2019

I feel more Examples should be given regarding the variable and bais tuning, also Error analysis videos should be a bit in-depth. Everything else is as good as it can get :)

By Jean M A S

Oct 27, 2017

The simulations were very good to build a good intuition about setting up a machine learning project.

But I regret that we didn't have coding exercises. 4 stars for this one.

By Carlos S C V

Apr 15, 2020

Me gustó el curso, pero creo que algunas lecciones fueron un poco más largas de lo necesario. Debo agregar que me gustaron mucho los simuladores, creo que me ayudaron mucho

By Vinod S

Nov 19, 2017

Helps clearly in understanding practical aspects of deep learning. An additional week, highlighting the aspects of productionizing a deep learning project would have helped

By Vinay N

Jul 12, 2020

Since I myself am working on a few projects, the concepts here are somewhat useful in error reduction. Especially when the models are used to automate medical applications

By Palathingal F

Sep 28, 2017

A unique course to understand the process of establishing a ML project. But lacks tools information and a more structured definition of the process. A bit too theoretical.

By Mahnaz A K

Jul 2, 2019

Thanks for the practical tips and insights from real projects.

Your pool of heroes of deep learning is very skewed. If the field is so skewed, then it's a bigger problem.

By Vivek V A

Feb 13, 2019

Good course for the ones who already started developing ML systems. This will help us in improving the ML systems and identify what can be done for which kind of problems

By Ivan L

Jun 25, 2019

Most of the material was quite useful, but some was, perhaps, too obvious. Also, some things were discussed too thoroughly, and, in my opinion, that was a waste of time.

By Алексей А

Sep 14, 2017

Would be great to obtain more concrete information.

For example, instead of "requires much more training data" to obtain "requires ~1'000'000 samples instead of ~100'000"

By Rafal S

Jul 22, 2019

Excellent content overall. However, reiterates some of the knowledge already presented in the two previous courses of the specialization. Lacks programming assignments.