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

35,320 classificações
3,706 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!

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3276 — 3300 de {totalReviews} Avaliações para o Structuring Machine Learning Projects

por Rafael G M

Dec 07, 2019

Providing further references would benefit this section


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


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.

por Cristina G

Oct 22, 2017

Concrete reminders of important and practical points

por Ktawut T

Oct 10, 2017

Very useful materials for leading a ML research team

por awalin s

Sep 29, 2017

interesting insights about real world implementation

por Mage K

Mar 07, 2018

Would've liked to have some programming assignments

por Carlisle

Aug 20, 2017

Introduced a lot on engineering project experiences

por Alvaro G d P

Nov 27, 2017

Interesting but perhaps we could have gone deeper.

por John H

Aug 26, 2017

Is the flight simulator hw going to be added soon?

por Pat B

Dec 08, 2019

Great course. I liked the compact, 2-week format.

por liu c

Mar 17, 2018

A little bit abstract. But still very inspiring!

por Florian M

Aug 24, 2017

Very interesting tools and ideas for applied ML.

por Jason G

Nov 25, 2018

Not as strong as the other 4 of 5 of the series

por Mark

Oct 13, 2018

Great course. Needs deeper practical examples.

por Francis J

Feb 25, 2018

A lot of insights rather than technical details

por Lukáš L

Jan 07, 2018

Coding exercises would be great in this course.