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Comentários e feedback de alunos de Structuring Machine Learning Projects da instituição deeplearning.ai

4.8
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35,304 classificações
3,703 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

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.

DC

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!

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3576 — 3600 de {totalReviews} Avaliações para o Structuring Machine Learning Projects

por Francesc C F

Aug 28, 2017

I missed the coding parts

por SUPARNA C

Dec 18, 2019

understood little bit

por 邹存安

Jan 08, 2019

可能是偏向实际应用,对我理论方向的帮助很少

por 禹宏康

Dec 10, 2018

讲道理这么课内容有点少,而且有点空....

por Pratik k c

Nov 05, 2017

Very Theoretical !!

por Tom M

Aug 25, 2017

I very simple class

por Mathew S

Jan 01, 2018

Its informational

por Siwei Y

Nov 28, 2017

就两周的课, 我不知道算是凑数吗

por Fotsing B K

Feb 25, 2018

to theoritcal

por Yide Z

Dec 17, 2017

too much bugs

por David B

Aug 19, 2019

No Homework!

por Sean L

Oct 06, 2019

Bit tedious

por Leticia L R

Aug 12, 2018

Bit boring.

por Wouter M

Jun 13, 2018

A bit short

por Zhen T

Dec 20, 2019

Too simple

por Gonzalo A M

Jan 17, 2018

Too short.

por My I

Mar 16, 2019

too easy

por Артеменко Е В

Sep 03, 2017

Too easy

por Sajal J

Oct 29, 2019

okay

por KimSangsoo

Sep 18, 2018

괜찮음

por Benedict B

Jul 27, 2018

ich

por Shawn P

Jun 08, 2018

k

por Daniel S

Mar 20, 2018

Definitely not worth paying for (and I literally completed this in one afternoon). Thankfully I did not pay, so it was not that bad value in fairness.

In honesty the lack of value from this course actually says a lot about Andrew Ng's original Machine Learning course, which was consistently excellent. Actually coding in Octave for that class cemented a lot of concepts as well, which this course does not.

The title of the course suggests this is pitched towards more advanced students who already know about Machine Learning but maybe not so much about best practices. This feels far too basic for that demographic. The practices are sensible though and useful, if maybe overly focussed on massive datasets as opposed to the ones that Google *doesn't* deal with on a daily basis. Things like SMOTE could have been mentioned as well, for example.

TL;DR: This feels like a missed opportunity. My advice is don't take it if you've done Andrew Ng's ML course. Google things after that and wait for a decent course that's pitched towards intermediate students.

por Marina R

Oct 18, 2017

I found the course rather confusing than helpful. One of the key issues with video-only courses is lack of interaction of the user with the material. In previous Andrew's ML courses, this issue was cunningly tackled with "wake-up" multiple choice mini-quizzes. Such techniques would help the course a lot.

The questions in the exam were poorly phrased and full of typos; some had numerical issues (percentage of errors in the dev set did not sum up). Some of the answers seemed to contradict with the material as I remembered it from the course: f.e., the question on whether to get more foggy images to improve the model performance should have been answered with "augmentation is fine as long as it looks fine to the human eye". This contradicts to Andrew's remarks in the course video "Addressing data mismatch" video -> Artificial data synthesis. Are you sure we would not introduce a bias by adding artificial fog to frontal camera images?

por Gilad F

Nov 17, 2019

Notwithstanding the great video lectures this course's assignments were poorly composed:

Firstly, there are no programming assignments! I understand the material here is mostly conceptual, however subjects such as 'Transfer learning' and 'Multi - task learning' should be given as a programming assignments. In 'Transfer learning' you need to modify an existing model, which I think is a good tool for a student. Hopefully we will use it in future lessons. Lastly some of the questions in both 'quizzes' have many complaints in the forum and the same complaints reappear yearly, therefor it's a bit annoying no measures are taken to modify the questions so they will be clearer.