Chevron Left
Voltar para Structuring Machine Learning Projects

Comentários e feedback de alunos de Structuring Machine Learning Projects da instituição

32,106 classificações
3,374 avaliações

Sobre o curso

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....

Melhores avaliações


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.


Mar 08, 2018

Going beyond the technical details, this part of the course goes into the high level view on how to direct your efforts in a ML project. Really enjoyable and useful. Thanks for making this available!

Filtrar por:

3251 — 3275 de {totalReviews} Avaliações para o Structuring Machine Learning Projects

por Fengxin Y

Sep 10, 2017

not that useful i thought

por Michael N

May 02, 2018

Okay, but lacks practicality

por Jason C

Dec 26, 2017

nice lectures and very useful knowledge learned by Andrew, but it is really short and no working assignment through real code.... and quite a lot more mistake than course1 and 2. Really love the two previous courses, don't work why the quality of the course drop off so sharply.

Somewhat disappointed, but still really great lectures.

por David P

Oct 17, 2017

Not nearly as good as the first two courses. These two weeks should probably be added into the second course at some point...

por Joshua P J

Jun 23, 2018

I fear this material won't challenge many learners. Feels like review.

por Matthieu D

May 13, 2018

I'm grading this course lower than I graded the two previous ones for two reasons: 1) while there are many examples given in the course, it is actually hard to take a step back and see how to concretely achieve some goals in a more generic manner, and 2) in the assignments (which are made of quizzes), many "wrong" answers would actually be appropriate if more context was given.

por Benedict B

Jul 27, 2018


por Mike T

Jul 24, 2018

I wished there were exercises besides the quizzes in this course

por Janet C

Jun 29, 2019

Overview of the machine learning process. No projects or sample code to actually organize the ideas into code.


Jul 20, 2019

Learned new things but the course was boring.......

por Andrew W

Aug 05, 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.

por Tzushuan W

Jun 01, 2019

Wordy and too abstract without hands on experience.

por Laurence G

Aug 12, 2019

Some interesting information in week 2 where multitask learning, transfer learning and end-to-end vs sequential nets are discussed. The bit on breaking down your errors into classes will also come in handy!

Week 1 was quite repetitive and seemed to be mostly common sense, probably could have cut these videos in half without losing much. Personally I watched most of this at 1.5x speed to avoid falling asleep. First quiz also had some less then conclusive answers - there's a lot of disagreement in the forums! Some issues also with the cutting of the videos, those these are only a minor nuisance overall.

Overall, less impressive then the other courses but still useful knowledge can be obtained here.

From a philosophical standpoint, I especially liked the 2 interviews in this course.

por David B

Aug 19, 2019

No Homework!

por Vincent P

Aug 24, 2019

Was really enthousiastic about the first two courses in the specialization, the third however felt a bit like going back a step in level of advancement.

por Abhijeet M

Jul 07, 2019

Informative but too short

por Nicolas

Jul 09, 2019

not as interesting as the other courses

por Diego P

Jul 12, 2019

Videos are quite long. Good course although a bit heavier than the previous ones

por 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!

por Wayne S

Sep 01, 2019

Video lectures tend to be repetitious, and can be confusing.

por Kanghoon Y

Sep 04, 2019

I got an intuitions from this lectures. But What I want to get from this lecture when I first saw the title, is the method how we can define the activation function at multi-task learning etc. In this video, I got only the overall flows.

por Bjorn E

Sep 09, 2019

Interesting and practical information, but it felt stretched out in an attempt to create a two-week course. With some editing and less repeated information this could be one week that would fit in the prior course.

por Liam A

Jun 15, 2019

Kinda boring, but still pretty practical.

por Hanbo L

Sep 22, 2019

Good non-technical materials, but short enough to be incorporated into other courses. Some aspects feel subjective. Many typos/minor mistakes in quizzes

por 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.