Chevron Left
Back to Structuring Machine Learning Projects

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

4.8
stars
49,614 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

ED

Aug 22, 2020

Excellent start for digging into topics that are not taught nowhere else. The author books 'Machine Learning Yearning' is a great next read that goes deeper in some of the aspects, really recommended.

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:

4651 - 4675 of 5,688 Reviews for Structuring Machine Learning Projects

By Lester A S D C

•

Jun 25, 2019

Useful knowledge regarding the efficient practices in the application of machine learning. Mentors doesn't seem as responsive though, compared to the other courses of the specialization. Quizzes were helpful, but needs more justification for some of the correct answers.

By Harshit S

•

Nov 12, 2017

The course showed the experiences while dealing with machine learning projects but could have been better if the experience would have been shared through practical exercises rather than objective case study.

It would be better if there were programming exercise as well.

By Jihwan M

•

Sep 15, 2017

I have a feeling that this third course is not yet fully edited. I see some black screens, and sometimes the clips have Andrew speak faster than usual. Nonetheless, the various tips and appropriate actions to take when doing a machine learning project were very useful.

By akshaya r

•

Jan 12, 2020

Good explanation for the initial steps of organizing the ML project and the direction to approach the problem accounted for. The quiz was interesting but as it is the same set of questions for any next attempt, I would not say I have mastered the course completely.

By Jean-Simon B

•

May 8, 2018

Only 2 weeks, good concepts to know. But videos are not "final release" they are not well edited. Some time Andrew repeat the same sentence 2x but they forgot to cut it.

No programming assignment. Although quiz format is fun and you really learn by doing the quiz.

By Bogdan P

•

Sep 3, 2017

This was a slightly more theoretical course than the first 3 in the Deep Learning specialization and, even thought I enjoyed it, I think the info would stick better if there would have been a programming assignment too (or some other type fo practical application).

By Kalle H

•

Nov 20, 2017

Nice and concrete examples of what to think of and focus on when trying to improve your machine learning projects. Not as engaging tasks to complete as in the previous courses in this specialisation, however a good change of scenary if you have been doing these.

By Boris V

•

Jan 21, 2018

Great material, but it's not quite easy to understand it from scratch, if you didn't have such problems yourself (i.e if you have no experience in deep NN training). I've stored this material and going to revisit it after I gain more experience in training NNs.

By Fredrik K

•

Oct 6, 2017

Great course, however the quiz of week 2 had some ambigious phrasings and I think at least one example (the one with the data synthesis of foggy images) is contradictive of what was taught in the video lessons. Other than that, really good content and teaching!

By Bharath S

•

Apr 20, 2019

A lot of concepts were put forward and taught well. If there was a programming assignment as well to back up the concepts that were taught like multi-task learning, how to deal with data mismatch, dividing the total data into train\train-dev\dev\test data etc.

By Sanskar A

•

Mar 22, 2020

I feel there is a glitch because even after completing the videos, it is not shown as completed and I had to replay them multiple times. Also there is a glitch in the assignment, because the correct answer in one attempt is shown as incorrect in the next try

By Eemeli L

•

Nov 19, 2019

Great and easy-to-follow introduction to structuring machine learning projects and focusing on what to tune on neural networks. One star left out because the content has not been polished, but there are minor errors here and there with separate corrections.

By Irene Z

•

Jun 8, 2019

The course seems a little less concrete than the others in this specialisation. But nevertheless, still a useful building block in anyone's deep learning repertoire. And note it will probably take less time to complete than the others, so plan accordingly.

By sakares s

•

Aug 24, 2017

It would be nice if there are hands on assignment or small projects on fine-tuning with existing weight you can found in the internet or multi-task learning project. Overall, it's a great course with many useful technique to try in the real world projects.

By Alejandro J C O

•

Feb 16, 2020

The course was really great, but a little part of the content was repeated from previous courses of the specialization. Also there should be more quizzes or exercises to master the large amount of practical advices for managing machine learning projects.

By Han T L

•

Mar 24, 2021

Very good class! It really hits me that AI programming is a different paradigm. Managing data is key.

That said, the materials in week1 have quite a bit of overlap with the 1st course (NN & DL). The materials should simply do a quick reminder a move on.

By Paul H

•

Dec 8, 2017

I liked this course, but not as much as the others. It is however setting the foundation for the remainder of the course material. It carries with it wisdom, which I think will make more sense at a later point when confronted with real life challenges

By Clint S

•

Mar 14, 2020

This is the course that really confirms Andrew Ng's grasp on the practically application of AI and ML. As long as you pay attention to what is said, you will get a lot from this course. I wish there was an edited collection of notes for this course

By Lars O A

•

May 29, 2018

Very useful part of the course set. Would like it to be slightly longer with more examples of TensorFlow and Keras. I felt that I put to much effort of trying to understand Keras in course number 5 instead of learning the principles and algorithms.

By John S

•

May 19, 2019

I like the "flight simulator" quizzes a lot and other courses might benefit from a similar assessment (in addition to regular quizzes and programming exercises), but I do think this course would benefit from some programming exercises too. Thanks!

By Mateo A R

•

Nov 18, 2020

It is a great course! thanks everyone involved in making it! If you can make more questions in every video or so i think it would be better. Also different case scenarios like the one you presented here in order for students to generalize better

By Scott B

•

Nov 12, 2019

I enjoyed the course content a lot, but noticed a lot of errors in test materials and test sections that didn't seem to make sense. For example, there were references to a flight simulator in quizzes that actually never appeared in any questions

By Novin S

•

Mar 3, 2018

I wish it could have had some coding practices and more mathematical insights. For instance, more insights on the different metrics (precision, recall, f-measure) and having some coding practices to get better sense of it on real world examples!

By Bo S

•

Dec 26, 2017

Very useful and practical information. Some of the videos had sound issues and minor editing glitches. I wish there had been more hands-on assignments. The few that were supplied were good but I think one more, less guided example would be good.

By 张之晗(ZhiHan Z

•

Aug 29, 2017

Comparing other courses before, it focus more on structing deep learning program and evaluate properly. However, the content in this week is really boring. In my opinion, it is better to imptove the course by teaching more implementation codes.