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学生对 提供的 Structuring Machine Learning Projects 的评价和反馈

45,739 个评分
5,219 条评论


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....


Mar 30, 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.

Jul 1, 2020

While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).


4626 - Structuring Machine Learning Projects 的 4650 个评论(共 5,162 个)

创建者 Wenhai Z

Nov 27, 2017

I would wish there were more programming practice assignments.

创建者 Kyle H

Oct 22, 2017

Contained all good content, there just wasn't very much of it.

创建者 Daniel A L G

Sep 21, 2020

The course is so useful, but sometimes the voice turns so low

创建者 Paweł P

Oct 11, 2019

Good overview of the problems occuring while training models.

创建者 Sathiraju E

May 25, 2019

Thank you, Andrew.

This course was very helpful though theory.

创建者 Daniel Z

Aug 14, 2018

Good course. It would be better with programming assignments.

创建者 Antonio H

Jul 2, 2018

I could not do the flight simulator test, it was not working.

创建者 Youjiang Y

Jan 16, 2018

It will be really nice if there is assignment for this course

创建者 Dioselin E B P

Jan 13, 2021

It's much information for only a one questionnaire for week!

创建者 zhang l

Mar 16, 2020

Some of the questions in the quiz set seems a bit confusing.

创建者 gayatri h

Jun 4, 2019

It gives the idea about real life machine learning problems.

创建者 David d V

May 24, 2018

It would have been great to add some programming assignments

创建者 Jeff O

Sep 11, 2017

Good material, just wish it had more "labs" to work through.

创建者 Juan C M S

Jun 8, 2020

In some occasions it's been has a bit redundant information

创建者 Pranav B

Jan 30, 2020

Good Course for Beginners need more programming assignments

创建者 Gil E B

Jul 29, 2019

Good Course to learn production pipelines for practical use

创建者 Fereydoon V

Feb 2, 2018

Hope we had programming assignment for this course as well!

创建者 Venkatesh N

Jan 3, 2018

Very good course, It should be last course in specilization

创建者 Richard M

Oct 14, 2020

Interesting ideas, but not as good structured as course 1.

创建者 Pakin S

Nov 28, 2019

Thanks I learn a lot of real world application and problem

创建者 Peter K

Mar 23, 2019

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

创建者 Akshat A

Feb 23, 2019

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

创建者 Joseph C

Apr 9, 2018

Needs programming exercises to help firm up the new ideas.


Mar 20, 2018

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

创建者 Alejandro R V

Jan 8, 2018

Not as interesting as the others, I personally prefer math