Chevron Left
Back to Structuring Machine Learning Projects

Learner Reviews & Feedback for Structuring Machine Learning Projects by deeplearning.ai

4.8
stars
40,709 ratings
4,507 reviews

About the Course

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization....

Top reviews

AM

Nov 23, 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.

JB

Jul 02, 2020

While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).

Filter by:

3526 - 3550 of 4,464 Reviews for Structuring Machine Learning Projects

By Joshua H

May 30, 2020

The course gave an extremely wholistic insight into what applying deep learning theory may be like in a commercial context. It felt as if Andrew left no stone unturned, answering every question a student could have either in the video, or in the weekly quizzes. The only adjustment I'd have liked to see is Andrew spending more time elaborating on multi-task learning networks (such as how to initialize back propagation along a network which uses multi-task learning).

By Andreea A

Feb 16, 2019

Liking this course is subjective. It is indeed based on the experience of others, but since experience can't always be generalized and transferred, the lectures are repetitive and bland (they are also badly edited in Week 2). On the other hand, the two "ML flight simulators" are really interesting and answering them is not obvious. It requires a lot of thinking and focus to choose correctly from apparently equivalent solutions, which might happen in real projects.

By Vrajesh I

Jun 04, 2018

Course was very theoretical as compared to the previous 2 courses in this specialization, and maybe a programming assignment could have been included (optional) where in the student could maybe learn how to play around with distributing the train/dev/test data and calculating the errors. Personally since I like hands on stuff more, these are my two cents on what could have been better :) amazing work by the teaching team and others on the backend as always!! :D

By Robin S

Jun 12, 2018

Once again, Andrew bringin' the heat!However, I docked a star for a couple of reasons. First off, I feel like there could be a bit more material here, perhaps an example notebook with noising and illustrating avoidable bias / variance / data mismatch.Most importantly though, I strongly, strongly recommend you go through the Week 2 Quiz (Autonomous Driving) and double check it for spelling/typing errors. There are quite a few of them!

By Rameses

Nov 15, 2019

Great practical advice on actually structuring and implementing machine learning projects. However the case study approach is more useful for people already in the field and working on projects than for some of us who are not yet in the field but attempting to gain exposure and knowledge in Machine Learning. I guess the value of these case studies will be more apparent when I actually start implementing ML projects in the real world

By Sebastian H

Apr 26, 2018

I find this course very relevant for practitioners. Perhaps from a team/organizational point of view it is the most relevant course. I agree that the concepts presented essentially distinguish the great from the average developer team. However, some of the material is very practical and I feel that right way to learn it is by doing it. To be fair it is very difficult to reflect that in a course! Overall I think it is very useful.

By P M K

Nov 30, 2017

Hi, This course though very useful had become a bit monotonous and at times a bit difficult to understand. There could have been better presentation giving more examples. The Quiz had really tough questions , in some cases the language is not clear. I request the course mentors to look into the same. Nevertheless, it has definitely been very useful as it highlights the practical problems faced and ways to resolve them

By Shuai X

Dec 15, 2017

This Course offers simple, useful and general tips for starting a typical deep learning project. The most valuable part is on how to split datasets and how to identify possible data distribution mismatch. The tips and case studies do not always work in real application. But that is perhaps because the course is intending to be simple. This course does not require any math backgrounds and can be completed in 4 hours.

By Harry ( D

Aug 12, 2018

Although I see other learners saying that this is the worse of all the Deep Learning specialization courses because there are no programming assignments, I believe it was a very useful course full of practical knowledge for properly structuring ML projects. I agree, however, that video quality is worse that the other courses and there are some editing issues (some video segments repeat, blank sections, etc.)

By David R R

Nov 17, 2017

This course is hard to complete because the lessons are very large and difficult to understand. However, I recommend this course for anyone than want to apply deep learning in real enterprise world.

Este curso es dificil de completar ya que las lecciones son muy largas y costosas de entender. Sin embargo, recomiendo el curso para todo aquel que quiera aplicar deep learning en el mundo empresarial.

By Jason T

May 27, 2018

I liked this course a lot, since it introduces transfer learning and multi-task learning and so moves you toward more powerful and realistic AI applications than the previous courses in the specialization. However, I missed the programming assignments that aided understanding so much in the previous courses. The quiz by itself was not as effective at illustrating the key concepts.

By Matt E

Apr 09, 2018

I wouldn't really consider this a "course," but the stuff he taught was great. However, Andrew could go much deeper into these topics. Some real data examples that he has come across would be even more helpful. Seeing how he codes his approaches in python would also be a very useful (and quick) batch of lectures. If he needed to extend it another week that would be understandable.

By Stoyan S

Oct 01, 2017

Excellent course just like the previous two. Short programming exercises would have been nice to have. Some of the answers in the quiz were too similar and this might be quite confusing for non-native English speakers and therefore can reflect more knowledge in English language rather than knowledge in related machine learning topics. I am looking forward for the next 2 courses.

By Mikko H

Sep 24, 2017

Great material that's clearly based on valuable practical experience. I and found the "machine learning flight simulator" quizzes to be an educational format. However, the editing of the quiz questions (grammar, matching question types with wording in the question etc) was not flawless in September 2017. This course would benefit from another review pass from this perspective.

By Kévin S

Jul 31, 2018

This course is clear, and show how a machien learning project should be driven. But there is two problem : First it is entierly theorical : no pratical exercices (so it is only 4 stars) ; second it did not speak of a big problem : How make your boss understand that if you use the "test" set too mush, it become another "developpement" set -> without using sciences words...

By 张子威

Mar 07, 2018

Overall, a great course for designing deep learning projects, which gives a lot of insights and tips that typically not taught at university classes. However, there does exist some minor problems related to video editing and quiz problems. I suggest the lecturer or staff of the course put more efforts in dealing with them (and maybe attend more to the discussion forums).

By Yen-Chung T

Sep 25, 2017

This course gives an overview on how to address common problems faced during machine learning projects. Although these experiences can prove valuable, for average people that may not be actively involved in machine learning, the information may sound like "common sense". The course may benefit with a more abundant set of real-world practice scenarios for analysis.

By Saurabh D

Apr 02, 2020

This course was totally different from the previous two courses. It was focused more on the theoretical aspects of how to approach and build ML projects, difficulties that ML engineers can face and how to avoid them. The content of this course could have been more interesting if more real world problems were included and if there were some programming exercises.

By KUMAR M

Feb 11, 2020

A very nice course to teach how to start a data science project, how to evaluate it, how to select path ahead improving the model, what all to be taken in consideration before training or while training or after training.

Some more case studies could be added since the course is smaller in length and case studies are helping a lot in making understanding clear.

By Carlson O

Oct 20, 2017

Again, great course. Congratulations. This time, i've missed some programming assignments, although the case studies was very instructive of the practice, some programming experiments with transfer learning will be great. Nevertheless, the course has extremely valuable knowledge to those, like myself, that want to practice in real problems and corporate world.

By Carlos d l H P

Jan 27, 2020

Actually adds some insights I hadn't learned (or at least I was guessing but it's always nice to have a double check) after 4 years as a data scientist.

Also, some of those insights are very specific to neural networks projects, so doesn't matter how many years have you been working if you've never made deep learning projects this will help you nevertheless.

By Ching-Chih L

May 17, 2018

This two-week course gave very important concepts. However, there's no programming assignments and lectures are lengthy. It felt a little "boring" for a hand-on guy like me.

That being said, one should not skip these important lessons if he/she wants to take charge of ML projects one day instead being a programmer who only takes orders from others for life.

By Arthur O

Feb 28, 2018

This course gave a lot of practical advice and is excellent material to combine with the more programming-focussed lectures of the deeplearning.ai series.

Small points of criticism are that I thought some videos could have been a bit shorter/less repetitive and there were quite a few language mistakes in the quizzes (missing words and grammatical errors)

By José D

Sep 26, 2019

Course 3 of the Deep Learning Specialization. There is no coding in this one but longer quizzes which require you to fully understand the concepts and recommendations given in the course. It's all about ML project strategy and how to manage you results and errors. Quite interesting and important for the general understanding of a Deep Learning project.

By Chris L

Sep 07, 2017

I liked this course overall and found it to be very informative. I, personally, was a little thrown by the eclectic nature of the course's materials. Sometimes it seemed as if the material covered in each week was only loosely related, or was thematically similar for part of the week, but then the last few videos were on something else entirely.