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Bewertung und Feedback des Lernenden für Structuring Machine Learning Projects von

48,348 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.


1. Juli 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!).

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5001 - 5025 von 5,522 Bewertungen für Structuring Machine Learning Projects

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

von Ktawut T

10. Okt. 2017

Very useful materials for leading a ML research team

von Awalin S

29. Sep. 2017

interesting insights about real world implementation

von Yu L

3. Apr. 2020

would like to have more excercise related to coding

von Mage K

7. März 2018

Would've liked to have some programming assignments

von Carlisle

20. Aug. 2017

Introduced a lot on engineering project experiences

von Marcelo A H

29. Mai 2020

Very interesting topics were shown in this course.

von William L

17. Apr. 2020

Very useful knowledge that is not commonly taught.

von Alvaro G d P

27. Nov. 2017

Interesting but perhaps we could have gone deeper.

von John H

26. Aug. 2017

Is the flight simulator hw going to be added soon?

von Pat B

8. Dez. 2019

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

von liu c

17. März 2018

A little bit abstract. But still very inspiring!

von Florian M

24. Aug. 2017

Very interesting tools and ideas for applied ML.

von Nicholas N S

28. Apr. 2021

There is so much noise in the explanation voice

von Jason G

24. Nov. 2018

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

von Mark

12. Okt. 2018

Great course. Needs deeper practical examples.

von Francis J

25. Feb. 2018

A lot of insights rather than technical details

von Lukáš L

7. Jan. 2018

Coding exercises would be great in this course.

von Mares B

17. Nov. 2020

A little short, maybe more hands on exercises?

von Ed G

8. Nov. 2020

Concise course with some interesting concepts.

von Tulip T

23. Juli 2019

Quite helpful when you start a new ML project.

von Ramakrishnan v

4. Nov. 2018

The session were simple, could be more complex