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Comentários e feedback de alunos de Structuring Machine Learning Projects da instituição

34,845 classificações
3,638 avaliações

Sobre o curso

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization....

Melhores avaliações


Nov 23, 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.


Mar 19, 2019

Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.

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3326 — 3350 de {totalReviews} Avaliações para o Structuring Machine Learning Projects

por Hwang E s

Oct 08, 2018

좋은 강의 였습니다.

por Mayur D

Aug 11, 2018

good course

por yuansj

Mar 16, 2018

Good course

por Kshitij T

Mar 08, 2018

good course

por Reshinth A

Dec 29, 2018

The best !

por Predrag S

Mar 10, 2018

Very good.

por Mohd F B Z

Aug 29, 2017



Apr 05, 2018

Thank you

por Abhijeet R P

Oct 19, 2017

Great! :)

por 舒意恒

Oct 14, 2017

very nice

por TianPing

Aug 27, 2017


por Yashika S

Sep 27, 2019

good one

por Xiong Z

Sep 04, 2019


por M N N

May 28, 2019


por mingwei Z

Sep 06, 2018

so well

por 靳雅麟

Dec 23, 2017


por Tất T V

Oct 15, 2017


por Takuya Kudo

Aug 10, 2019


por Riyaz A

Sep 22, 2017







Oct 23, 2019


por zhesihuang

Mar 03, 2019



Jul 08, 2018


por David R

Oct 01, 2019


Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it. Not sure I would have taken it as a stand-alone course, though.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended

por Felix E

Oct 09, 2017

This is a 2-week follow-up on the previous two courses in this specialization.

While it's a decent course that goes over a few interesting topics, I have a hard time giving it more than three stars. Reasons for that are below:

(1) Especially the first week felt very slow and repetitive. Most of the material could have been summarized a much smaller timeframe.

(2) The course went over some interesting topics in a very high-level way, but skipped a lot of the details that would have been very interesting to people looking to learn deep learning in depth (like the target audience of this course!).

(3) While I think the approach of having some themed case studies for the test is neat, a lot of the answers left me thinking "well, the correct answer would also depend on X which isn't specified". Good concept to test knowledge in a "discussion/oral exam" session, but IMHO bad for hard "wrong or right" multiple choice tests.

(4) Some videos had "black screen" times at the end, errors, cut-offs and repetitions were not cut out, and overall I think this had the least amount of "polishing" of the courses in this specialization so far.

I'd have preferred if the content of this course were a bit more steamlined and merged it into the other courses of this specialization.

por Aristotelis-Angelos P

Jul 07, 2018

Overall, I think that it was a good course but in my opinion, the knowledge of this course cannot be easily transferred to people with very few experience in Machine Learning. Therefore, I was wondering whether it should be the 3rd course or the 5th course in this Deep Learning Specialization! Moreover, in order for someone to deeply comprehend these concepts such that he/she is able to apply them in a Machine Learning project, he/she should work on a project on his own where he/she will meet these concepts and will have to search in order to solve them.Last, personally, even though I am quite satisfied from the courses, I would expect that one more course is added to Coursera which is going to require to build a Deep Learning project! I think that this course should be of more advanced level and require (not Intermediate as those ones) and should require from students to build projects like the ones builded in the cs230 class from Stanford.Greetings from a PhD USC student