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

4.8
stars
49,968 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

TG

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.

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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5351 - 5375 of 5,727 Reviews for Structuring Machine Learning Projects

By Yechan K

•

Jun 17, 2020

Practical

By IURII B

•

Apr 5, 2018

Thank you

By Abhijeet R P

•

Oct 18, 2017

Great! :)

By 舒意恒

•

Oct 14, 2017

very nice

By TianPing

•

Aug 26, 2017

内容稍稍有点重复。

By Sami U

•

Oct 10, 2024

Too easy

By Dave L

•

Jul 9, 2020

verygood

By Yashika S

•

Sep 27, 2019

good one

By Xiong Z

•

Sep 3, 2019

helpeful

By Naveen N

•

May 28, 2019

Awesome!

By mingwei Z

•

Sep 6, 2018

so well

By Jin A

•

Dec 23, 2017

没有中文字幕

By Tất T V

•

Oct 15, 2017

Useful

By Takuya　Kudo

•

Aug 10, 2019

Cool.

By Riyaj A

•

Sep 22, 2017

g

r

e

a

t

By Ansuman B

•

Mar 23, 2021

good

By SEUNGMO O

•

Oct 30, 2020

good

By Akash K

•

Aug 13, 2020

Good

By Alaa E B

•

Jun 23, 2020

good

By Krishna P D C

•

May 2, 2020

Good

By Annaluru K

•

Apr 17, 2020

Well

By VIGNESHKUMAR R

•

Oct 23, 2019

good

By zhesihuang

•

Mar 3, 2019

good

By CARLOS G G

•

Jul 8, 2018

good

By Felix E

•

Oct 9, 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.