Chevron Left
Back to Structuring Machine Learning Projects

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

4.8
stars
46,459 ratings
5,325 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

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.

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.

Filter by:

4926 - 4950 of 5,270 Reviews for Structuring Machine Learning Projects

By Abhijeet R P

Oct 18, 2017

Great! :)

By 舒意恒

Oct 14, 2017

very nice

By TianPing

Aug 27, 2017

内容稍稍有点重复。

By Dave

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 靳雅麟

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 CK P D

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.

By Aristotelis-Angelos P

Jul 6, 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

By Todd J

Aug 22, 2017

The content in this course is excellent; however, the learning activities are insufficient for truly internalizing the material and do not follow evidence-based guidelines for learning (see the book, Make it Stick). The video lectures cover a lot of ground, but I found that many were a bit too long, often dwelling on points well after they were made. The problem is that the only actual learning activity is a 15 question multiple choice at the end of each week (and there are only two weeks of material). The course would really benefit from having questions embedded in the videos, similar to Udacity style courses. Following those with the 15 question "simulator" would then reinforce the material. However, this course also needs programming assignments at the end of each week so that students can actually gain real experience with the methods and suggestions.

By Anne R

Sep 25, 2019

Good general information is provided but this material could be layered into the other courses in this specialization. I would recommend that the case studies be based on real industry problems that present the backstory of the decisions the teams made. Also programming assignments would be useful in which the impact of incorrectly classified training data is studied in detail or in which images that have been synthesized are used versus not used. It did not take too much time to work through this course so the information provided is worth the cost, but I am not convinced that this series is viewed as more than an opportunity to make some money off of the name brand. Much of the information provided so far is covered in the Deep Learning - Goodfellow text and the extras are vague and repetitive.