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

4.8
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36,102 classificações
3,847 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.

MG

Mar 31, 2020

It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.

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3401 — 3425 de {totalReviews} Avaliações para o Structuring Machine Learning Projects

por Gil E B

Jul 30, 2019

Good Course to learn production pipelines for practical use

por Fereydoon V

Feb 03, 2018

Hope we had programming assignment for this course as well!

por Venkatesh N

Jan 04, 2018

Very good course, It should be last course in specilization

por Pakin S

Nov 28, 2019

Thanks I learn a lot of real world application and problem

por Peter K

Mar 23, 2019

강의 후반부 (2주차) 에 강의 속도가 인위적으로 조정된거 같습니다. 속도가 빨라 이질감이 느껴졌습니다.

por Akshat A

Feb 23, 2019

Curated content, quite exclusive indeed. Respect to Dr. Ng

por Joseph C

Apr 10, 2018

Needs programming exercises to help firm up the new ideas.

por QUINTANA-AMATE, S

Mar 20, 2018

Completely new of what it is out there. Well done Andrew!!

por Alejandro R V

Jan 08, 2018

Not as interesting as the others, I personally prefer math

por Gopala V

Oct 24, 2017

Gave some ideas on mismatched data and how to address them

por Roberto J

Oct 19, 2017

A bit dry, would love to see some more concrete examples.

por Vinicius B F

Oct 23, 2017

Content was fantastic, but the videos were badly edited.

por Suresh P I

Sep 10, 2017

Can be potentially folded into other courses if possible

por heykel

Jan 27, 2020

very helpful to build an intuition for DL strategies...

por Rafael G M

Dec 07, 2019

Providing further references would benefit this section

por WEIJIAN K

Nov 15, 2017

You can know well a lot of strategy in machine learning

por 王毅

Dec 24, 2019

the content is good, but the videos are not well made.

por Shuochen Z

Feb 18, 2019

内容架构很好,讲得也很实用,但觉得课时有些短,许多重要且有趣的问题都未能得到展开详述。期待后续的扩充课程~~

por Gundreddy L M

Sep 11, 2018

excerice should be given for this one proper user case

por Alexey S

Oct 23, 2017

Good class, but 2 previous are much better and useful.

por Lei C

Sep 25, 2017

the answer of the assignment is a little bit arguable.

por diego s

Feb 18, 2020

I miss notebooks for practice, besides questionnaires

por Xinghua J

Sep 06, 2019

If there is some coding practice, it would be better

por Hee S K

Apr 18, 2018

It is an unique lecture providing empirical advises.

por Pablo L

Oct 30, 2017

Great set of guidelines. Mostly theoretical, though.