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

48,224 ratings
5,531 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


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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4501 - 4525 of 5,498 Reviews for Structuring Machine Learning Projects

By Bharath S

Apr 20, 2019

A lot of concepts were put forward and taught well. If there was a programming assignment as well to back up the concepts that were taught like multi-task learning, how to deal with data mismatch, dividing the total data into train\train-dev\dev\test data etc.

By Sanskar A

Mar 22, 2020

I feel there is a glitch because even after completing the videos, it is not shown as completed and I had to replay them multiple times. Also there is a glitch in the assignment, because the correct answer in one attempt is shown as incorrect in the next try

By Eemeli L

Nov 19, 2019

Great and easy-to-follow introduction to structuring machine learning projects and focusing on what to tune on neural networks. One star left out because the content has not been polished, but there are minor errors here and there with separate corrections.

By Irene Z

Jun 8, 2019

The course seems a little less concrete than the others in this specialisation. But nevertheless, still a useful building block in anyone's deep learning repertoire. And note it will probably take less time to complete than the others, so plan accordingly.

By sakares s

Aug 24, 2017

It would be nice if there are hands on assignment or small projects on fine-tuning with existing weight you can found in the internet or multi-task learning project. Overall, it's a great course with many useful technique to try in the real world projects.

By Alejandro J C O

Feb 16, 2020

The course was really great, but a little part of the content was repeated from previous courses of the specialization. Also there should be more quizzes or exercises to master the large amount of practical advices for managing machine learning projects.

By Han T L

Mar 24, 2021

Very good class! It really hits me that AI programming is a different paradigm. Managing data is key.

That said, the materials in week1 have quite a bit of overlap with the 1st course (NN & DL). The materials should simply do a quick reminder a move on.

By Paul H

Dec 8, 2017

I liked this course, but not as much as the others. It is however setting the foundation for the remainder of the course material. It carries with it wisdom, which I think will make more sense at a later point when confronted with real life challenges

By Clint S

Mar 14, 2020

This is the course that really confirms Andrew Ng's grasp on the practically application of AI and ML. As long as you pay attention to what is said, you will get a lot from this course. I wish there was an edited collection of notes for this course

By Lars O A

May 29, 2018

Very useful part of the course set. Would like it to be slightly longer with more examples of TensorFlow and Keras. I felt that I put to much effort of trying to understand Keras in course number 5 instead of learning the principles and algorithms.

By John S

May 19, 2019

I like the "flight simulator" quizzes a lot and other courses might benefit from a similar assessment (in addition to regular quizzes and programming exercises), but I do think this course would benefit from some programming exercises too. Thanks!

By Mateo A

Nov 18, 2020

It is a great course! thanks everyone involved in making it! If you can make more questions in every video or so i think it would be better. Also different case scenarios like the one you presented here in order for students to generalize better

By Scott B

Nov 12, 2019

I enjoyed the course content a lot, but noticed a lot of errors in test materials and test sections that didn't seem to make sense. For example, there were references to a flight simulator in quizzes that actually never appeared in any questions

By Novin S

Mar 3, 2018

I wish it could have had some coding practices and more mathematical insights. For instance, more insights on the different metrics (precision, recall, f-measure) and having some coding practices to get better sense of it on real world examples!

By Bo S

Dec 26, 2017

Very useful and practical information. Some of the videos had sound issues and minor editing glitches. I wish there had been more hands-on assignments. The few that were supplied were good but I think one more, less guided example would be good.

By 张之晗(ZhiHan Z

Aug 29, 2017

Comparing other courses before, it focus more on structing deep learning program and evaluate properly. However, the content in this week is really boring. In my opinion, it is better to imptove the course by teaching more implementation codes.

By Santiago R A

Dec 2, 2017

Some questions in week 2 test are ambiguous and the last videos have edition errors. But overall strategies for guiding projects are very useful. It's a great course about practical aspects of Deep Learning you'll probably not find anywhere.

By Akansh M

Jun 12, 2020

I have previously worked on DL project and its performance was not good in real-world data, I wasn't able to draw any reason for it. This course taught me how to deal with such kind of problem and how one can approach the possible solution.

By Max

Jun 17, 2018

Was a good course with a lot of useful tips that I am sure I am able to use in my job as a data scientist. However, I would've liked if there were a few more hands-on examples (e.g. using jupyter) to really drives these concepts more home.

By Nityesh A

Oct 10, 2017

The course could have been much shorter than it is because Andrew seems to be repeating his simple ideas a lot in the lectures. However, each simple advice seems important for practical purposes (I am willing to take Andrew's word for it).

By Mikhail F

Oct 20, 2019

It might be not that trivial. But some hand-on experience with some code might be good here as well. As many practice as possible would be beneficial to the learners, coupled with great explanations from Andrew that are already in place.

By Alon S

Sep 23, 2019

I think the quizzes should be considerably longer, to include more scenarios, and also have fewer questions that rest on technicalities (where some of the answers are almost correct except they misuse a term or give a wrong description).

By Michael T

Oct 26, 2017

While the simulation is unique and very useful feature of this specialization. I believe examples with data would add to the leaning experience by allowing a student to actually run the scenarios and experience the qualitative changes.

By Mark M

Nov 21, 2017

This course is at all an important part during the learning journey. The only reason why I not rate full 5 stars that the recommendation ramen little bit on high level and do not address typical frame conditions in real world projects.

By olimoz

Aug 16, 2017

Lots of practical stuff about training models. But you should try building a few models before doing the course. Otherwise, you may not fully appreciate how much time can be wasted unless you use Andrew's clear and logical approaches.