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

4.8
stars
47,503 ratings
5,446 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.

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4526 - 4550 of 5,411 Reviews for Structuring Machine Learning Projects

By Mahnaz A K

Jul 2, 2019

Thanks for the practical tips and insights from real projects.

Your pool of heroes of deep learning is very skewed. If the field is so skewed, then it's a bigger problem.

By Vivek V A

Feb 13, 2019

Good course for the ones who already started developing ML systems. This will help us in improving the ML systems and identify what can be done for which kind of problems

By Ivan L

Jun 25, 2019

Most of the material was quite useful, but some was, perhaps, too obvious. Also, some things were discussed too thoroughly, and, in my opinion, that was a waste of time.

By Алексей А

Sep 14, 2017

Would be great to obtain more concrete information.

For example, instead of "requires much more training data" to obtain "requires ~1'000'000 samples instead of ~100'000"

By Rafal S

Jul 22, 2019

Excellent content overall. However, reiterates some of the knowledge already presented in the two previous courses of the specialization. Lacks programming assignments.

By Amir R K P

Dec 7, 2018

I wish there was more examples, visualization and depiction of work with referral to papers or experiment here. or perhaps a bit of project management, data management.

By Pete C

Jun 24, 2018

Enjoyable, but felt a little less challenging and more hastily assembled. Regardless, the material is valuable and as always, a pleasure to be instructed by Andrew Ng.

By Lars R

Aug 29, 2017

The course material is relevant and useful, however, I agree with other reviewers that these 2 weeks should rather be a 1-2 weeks addition to one of the other courses.

By Andrew R

Apr 30, 2018

Quick course. Worth taking because gives some practical guidance on what avenues to pursue when finding a optimal model (which takes into account human time required)

By Poorya F

Dec 10, 2017

The first week is too long with repetitive materials. The second week is very interesting. However, I wish the course was designed such that it required some coding.

By Hany T

Aug 27, 2019

Great course, great professor .. the only issue is that I feel sleepy every time I watch the videos :), it's some how single tone. Also the audio could be improved.

By Karthikeyan C (

Mar 16, 2020

It is always important to learn above the problem-solving methods and tools. This course teaches the complete diagnosis methodologies for Machine Learning problems

By Mehran M

Jun 25, 2018

Overall, very informative, however I think the content of this course could be divided between the first and the second course.More assignments would've been nice.

By Rajesh R

Nov 26, 2017

Lots of practical advice and ideas on how to work on actual projects and things to look out for. Great stuff. Wish it had a few programming exercises or a project.

By Ross K

Aug 30, 2017

Useful introduction to meta-level principles of machine learning process management, but not quite as groundbreaking or well-instructed as the previous two courses

By kArThIk T

Apr 13, 2020

A real time project or programming assignment could improve our confidence level.

All of these courses if it had readable material along with video, it'd be great.

By SYZ

Dec 9, 2018

Hope to have coding practices for the second week's materials.

Anyway, the current course is already very helpful. Thanks to Andrew and all staff behind the scene!

By Jussi V

Feb 18, 2018

Content is good, but a bit thin... This course makes sense as part of the deep learning specialisation, even if this is a bit too short to be a course of its own.

By Boris D

Jul 23, 2019

A bit less interesting than the others I think. To me the whole first week was full of obvious stuff. The second week, however, was very interesting and helpful.

By Subash P

Oct 23, 2017

There was lot of theory and probably not one of my strengths. However the content is very useful for bringing some structure to machine learning problem solving.

By Jaime R

Nov 20, 2018

This course could have just been an extra week or two of course 2. It doesn't have the depth of the others, although it is very practical and I like the content

By Calvin K

Mar 4, 2018

Good advice on how to work on a machine learning project from the ground up. Tho most of the material is already covered in Ng's Machine Learning Yearning book.

By Deleted A

Nov 19, 2017

Nice to see a course on machine learning about the 'other stuff' around machine learning. However, links didn't work half the time and it was a bit unpolished.

By Klas K

Oct 13, 2017

Some of the lectures feel quite lengthy and repeat stuff. It seems to be easily possible to condense into one week which could be added to the previous course.

By anahita p

Jan 19, 2019

a lot of topics are covered in machine learning course, but this has an upgrade to input from previous course due to changes has happened in AI in last years.