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

47,197 classificações
5,421 avaliações

Sobre o curso

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Melhores avaliações

30 de Mar de 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.

22 de Nov de 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.

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4851 — 4875 de 5,377 Avaliações para o Structuring Machine Learning Projects

por Suresh P I

10 de Set de 2017

Can be potentially folded into other courses if possible

por Hanqiu D

9 de Jan de 2021

It's too easy and cannot be a reasonable single course.

por heykel

27 de Jan de 2020

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

por Rafael G M

7 de Dez de 2019

Providing further references would benefit this section


15 de Nov de 2017

You can know well a lot of strategy in machine learning

por B S K

14 de Jul de 2020

Good teaching of practical approaches and nice quizzes

por 王毅

24 de Dez de 2019

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

por Shuochen Z

17 de Fev de 2019


por Gundreddy L M

11 de Set de 2018

excerice should be given for this one proper user case

por Alexey S

22 de Out de 2017

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

por Lei C

25 de Set de 2017

the answer of the assignment is a little bit arguable.


6 de Out de 2020

Content is good. Presentation could have been better.

por Kumari P

28 de Mai de 2020

machine learning project are highly iterative as you.

por diego s

18 de Fev de 2020

I miss notebooks for practice, besides questionnaires

por Xinghua J

6 de Set de 2019

If there is some coding practice, it would be better

por Pranjal V

11 de Jul de 2020

Very well explained but needs more reading material.

por Hee s K

18 de Abr de 2018

It is an unique lecture providing empirical advises.

por Pablo L

30 de Out de 2017

Great set of guidelines. Mostly theoretical, though.

por Cristina G F

22 de Out de 2017

Concrete reminders of important and practical points

por Ktawut T

10 de Out de 2017

Very useful materials for leading a ML research team

por Awalin S

29 de Set de 2017

interesting insights about real world implementation

por Yu L

3 de Abr de 2020

would like to have more excercise related to coding

por Mage K

7 de Mar de 2018

Would've liked to have some programming assignments

por Carlisle

20 de Ago de 2017

Introduced a lot on engineering project experiences

por Marcelo A H

29 de Mai de 2020

Very interesting topics were shown in this course.