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

4.8
stars
46,470 ratings
5,329 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

AM
Nov 22, 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.

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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5001 - 5025 of 5,272 Reviews for Structuring Machine Learning Projects

By Francisco S R

Oct 25, 2017

The course was just a bunch of tips and suggestions. Yes, they are useful, but given the empirical nature of machine learning I would expect those tips to be accompanied by practical applications and homework.

By Amit P

Aug 21, 2017

I expected more. The videos were a little long and repetitive. The content was important, though. Maybe the course materials could be squeezed into one week and combined with the previous deep learning course.

By Viswajith K N

Jun 24, 2018

THe course was challenging and had valuable inputs. But it would be even more wonderful if we got to work on some portion of the case studies as a capstone project at the very least. Else Its a 5 star course.

By daniele r

Jul 15, 2019

Good for the numerous hints about practical issues such as different distributions on train/dev/set. Very bad for the lack of hands-on assignments. Good practical advices but no occasion to see them working!

By John O

Dec 16, 2017

The quality of the course is not up to par with the other courses in the specialization. There is very little content and it is gone through too slowly. There are also more bugs and errors in the exercises.

By 臧雷

Sep 5, 2017

Most of the materials in this course is tedious and have already been taught in previous courses. But I suggest the Transfer Learning and Multi-task Learning part, as well as the end-to-end learning part.

By Wells J

Dec 16, 2017

The course was misleading on what homework there was (machine learning flight simulation?) There was no homework. and the lectures were pretty bland compared to other courses in this specialty.

By Karthik R

Mar 4, 2018

Transfer Learning and Multi-Task learning discussed in the course would greatly benefit from having programming assignments where people can play around with the data and learn confidently.

By Andrew W

Aug 5, 2019

Good information about how to structure projects and how to boost performance. Not very hands-on however. Fits in well with the Specialization though as a break before CNN's and sequences.

By Daniel C

Nov 19, 2017

Not as helpful... just a few suggestions and ideas... but there's no great application of the information learned here like a walk-through project or something with code, that's graded.

By Luiz C

Oct 22, 2017

less useful than previous courses.

Would appreciate to go much deeper in directions like CNN, RNN, RL and review Unsupervised Learning (which was too light, ... no mention about RBM)

By Akshay S

Jun 18, 2020

It was very theoretical and subjective.

It would be useful if the learner has some more experience in DNN than currently expected.

But I definitely enjoyed 2nd week of the course.

By Andrew C

Oct 29, 2017

Interesting content, but lacking in real work. As with all of the deeplearning.ai courses thus far, the multiple choice questions are frequently ambiguous and poorly worded.

By Zsolt K

Sep 24, 2018

The information is really basic, most of it is self explanatory. This shouldn't be a course on its own, rather maybe a week/half weeks worth of material in another course.

By Sherif A

Nov 25, 2017

This course is too subjective. Andrew shares his experience in a structured way in the lecture. However, I feel that correct structuring decisions need to be brainstormed.

By Patrick F

Dec 12, 2019

Seeing different practical use scenarios and adaptions is fine but it got pretty boring without a real application to tune. The Quizzes on the other hand were very good!

By Alberto S

May 29, 2018

Although everything taught is relevant, it was too much theoretical. And some of the evaluation questions are not clear (well, at least for non native English speakers).

By Daniel V

Feb 26, 2020

Generally useful skills, but the contents partially overlap with previous courses and the overall quality doesn't match the previous courses (eg poor video mastering).

By Davide C

Nov 26, 2017

The course was interesting, but in my opinion too theoretical. I preferred the first 2 courses with Python programming. I am now looking forward to the next 2 courses.

By Felipe L d S

Jun 7, 2018

Even though some of the content is useful, I feel like this course should be merged with the second one. There is not new information enough to justify a new course.

By Thomas J

Jan 24, 2018

Good material was presented in this course but there were a number of technical errors in the video recordings. If they were cleaned up this course would be perfect.

By Jose P

Sep 30, 2019

Topics are a bit vague, which is fine as the content is interesting and useful nonetheless, but perhaps exposition is too lengthy relative to the amount of content.

By Robbin R

Feb 17, 2018

Gives good insights on how to work on a Machine Learning project yet. Provides some rule of thumbs for different hick-ups that may be encountered during a project.

By Nick S

Sep 8, 2017

even though there are great tips and advices, it does not justify an entire course and they can be mentioned in 3 videos so a lot of the videos were repetitive.

By Kan X

Feb 18, 2020

I like this specialization in general. However, this third one has too many overlapping contents and some videos are not that useful. Just personal opinion.