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Bewertung und Feedback des Lernenden für Structuring Machine Learning Projects von

48,880 Bewertungen

Über den Kurs

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....



22. Nov. 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.


30. März 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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5026 - 5050 von 5,575 Bewertungen für Structuring Machine Learning Projects


20. März 2018

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

von Alejandro R V

8. Jan. 2018

Not as interesting as the others, I personally prefer math

von Gopala V

24. Okt. 2017

Gave some ideas on mismatched data and how to address them

von Akshita J

23. Apr. 2020

An assignment could have been included to let practically

von Roberto J

19. Okt. 2017

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

von Vinicius B F

22. Okt. 2017

Content was fantastic, but the videos were badly edited.

von Suresh P I

10. Sep. 2017

Can be potentially folded into other courses if possible

von Hanqiu D

9. Jan. 2021

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

von heykel

27. Jan. 2020

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

von Rafael G M

7. Dez. 2019

Providing further references would benefit this section


15. Nov. 2017

You can know well a lot of strategy in machine learning

von Sreenivas K

14. Juli 2020

Good teaching of practical approaches and nice quizzes

von 王毅

24. Dez. 2019

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

von Shuochen Z

17. Feb. 2019


von Gundreddy L M

11. Sep. 2018

excerice should be given for this one proper user case

von Alexey S

22. Okt. 2017

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

von Lei C

25. Sep. 2017

the answer of the assignment is a little bit arguable.


6. Okt. 2020

Content is good. Presentation could have been better.

von Kumari P

28. Mai 2020

machine learning project are highly iterative as you.

von diego s

18. Feb. 2020

I miss notebooks for practice, besides questionnaires

von Xinghua J

6. Sep. 2019

If there is some coding practice, it would be better

von Pranjal V

11. Juli 2020

Very well explained but needs more reading material.

von Hee s K

18. Apr. 2018

It is an unique lecture providing empirical advises.

von Pablo L

30. Okt. 2017

Great set of guidelines. Mostly theoretical, though.

von Cristina G F

22. Okt. 2017

Concrete reminders of important and practical points