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

4.8
stars
46,746 ratings
5,353 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

JB
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!).

MG
Mar 30, 2020

It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.

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5001 - 5025 of 5,300 Reviews for Structuring Machine Learning Projects

By Sagar B

Oct 29, 2017

The course work is really good. It has a practical emphasis. However, I did not like the quizzes (especially week 2 quiz) in the sense that the options are not very clear to understand and you end up being more confused. I hope the team works on the clarity of options for people who take it in future.

By Fabian A R G

Oct 28, 2017

Even though the materials in the course are very interesting, I would expect that in the third course we would have more tools in order to work by ourselves in a project... It would have been amazing a final project where you can put together this tools. Nevertheless it is still an interesting course.

By David B

Oct 6, 2017

This course was less satisfying then the 2 previous in the specialization. A lot of repetitions, no programming exercices. Interesting test cases but feels a little out of scope because we have not done image and speech reccon yet. Consider putting the course at the end of the specialization maybe?

By kritika

Mar 26, 2019

I think the week 1 was overstreched. There was not much content to deliver and for the first time Andrew's classes made me sleep. It was like the boring lectures we get at school. I think we can easily shorten the length of this course or just scrape it and add it to course 2.

By Andrej P

Jan 26, 2018

I found this course to be a bit confusing with regards to what data set (training/dev/test) to fix under what conditions and so on. I've also missed having a practical home work, the case studies were fine, but I find that practical applications help me remember things better.

By Filip R

Mar 18, 2020

Some of the quiz questions (especially in the first week) were quite ambiguous. If I did not take the quiz directly after the videos, I don't believe I would be able to pass, Also some written summaries as in the 1st Ng's Machine Learning course would be helpful.

By Joshua O

Oct 19, 2018

Some helpful advice here and there, but a lot of it seemed like common sense. It was not that difficult and a tad boring. Would maybe benefit from having us do actually data collection and cleaning tasks, or implement a ML pipeline and monitoring for the pipeline

By Kaitlin P

Dec 13, 2017

Generally provides very good advice. Perhaps this course better placed at the end of the course as there isn't much hands-on experience involved and students would benefit form having experience with CNN's and RNN's prior to thinking on project-level scales.

By Jacob T

Nov 29, 2017

Too many broad statements of "yeah, we generally do this thing for best results" with very little explanation of the background theory. I don't expect advanced math and derivations, but better intuition into why certain best practices exist would be nice.

By Vijay A

Dec 23, 2019

This course was good, but it was pretty light on content to be considered a separate course by itself. Though the content is valuable, it could've been included as additional/bonus content on either of the first two courses in the DeepLearnign.ai series.

By Tom B

Apr 13, 2018

I didn't find this course as engaging as Course 1 -- there weren't any coding exercises and it felt like a bit of a let-down after the excitement of coding in Course 1. But it may turn out to have value when trying to start a new AI project from scratch.

By Francesco B

Oct 6, 2017

This course felt a bit "padded" compared to the previous ones. Also the lack of programming exercises made it seem more theoretical. Finally, the material seems rushed, e.g. there are mistakes in the video editing, strangely long pauses by the teacher.

By Peter G

Dec 5, 2017

Many helpful insights and advice from an experienced person is always great, but I don't thing this can be qualified as a complete 'course'. As I now see it - Course 2 and 3 of this specialization could easily be merged into one without loosing much.

By Maulik S

May 31, 2020

The course should have had at least two more quizzes to understand the content better. Also, I would suggest adding programming exercises that help to better explore the ideas of orthogonality, train-dev set correction, and data synthesis.

By Kanghoon Y

Sep 4, 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 Jatin s

Aug 27, 2020

This course to me seemed a bit too much theoretical.This could have been a little more assignment weighted so as to bring more focus to study and practise.Overall the case studies were pretty thorough to cover the course material.

By Abhishek S

May 11, 2020

I think that a lot of this knowledge would have been useful had it been given after building a few projects ourselves (i.e - sample projects), I could not feel connected with the content much and was a little uninteresting for me.

By SHUBHAM G

Jun 22, 2018

The course must have had some coding exercises showing how wrong the error analysis doesn't work and also some exercises on transfer learning, multi-task learning in order to see in practice how these concepts work in real life.

By Mats B

Mar 30, 2019

This course did not really feel like a course, just videos and ambiguous quizzes. Some repetition and poor editing of the videos. I recommend to reformat this course to be more substantial and to include programming exercises.

By Мар'ян Л

Jun 2, 2018

Compare to other courses of the specialization, this has lower quality of video lectures, often repeats things from previous courses and I think it would be better to separate whole course as a separate week of a previous one.

By Gianfrancesco A

Oct 23, 2017

Very interesting course about guidelines about how to set up a project target oriented, not so trivial. Perhaps an improvement could be to add a chapter on the various DN architectures available for the various tasks.

By Lukas O

Dec 10, 2017

Would be much better if it included a programming assignment as a final project. I'd like to have a little less scaffolding during the decision-making process to see how well I can do on even more realistic problems.

By Gabriel S M

Oct 22, 2017

It is a good course because it highlights practical aspects of implementing ML. Some of the test questions were a bit ambiguous though.

I'd also like to have seen Transfer/Multi-task learning implementation exercises.

By Noga M

Jul 21, 2020

I understand why this course is important, but for me it was the least favorite course so far. Some of the videos were too long and repeat themselves. Maybe it's because I have knowledge in machine learning already.

By ነቅዕ ን

Feb 12, 2018

This course is full of intuitions that are very difficult to remember at once. The quiz is very hard and mind teasing. For better confidence, I would like if you add one more case study.

In general the course is good