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Opiniones y comentarios de aprendices correspondientes a Ingeniería de características por parte de Google Cloud

4.5
estrellas
1,679 calificaciones
182 reseña

Acerca del Curso

Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering where we will discuss good vs bad features and how you can preprocess and transform them for optimal use in your models....

Principales reseñas

GS
8 de abr. de 2020

This course covers a lot about the data pre-processing, and the tools available in Google Cloud to enable the gruelling tasks. Thanks very much for the lectures and training labs. Very informative.

OA
25 de nov. de 2018

It's a pretty interesting course, specially that's the only one that teaches featuring engineering with a focus on production issues, but it assumes some knowledge with apache beam, and dataflow.

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151 - 175 de 183 revisiones para Ingeniería de características

por irfan s p

10 de abr. de 2020

maybe this course is very good, but for me I really hard to digest knowledge from this course. It needs a lot of time to understand the theory. Maybe it will be good if the course is given in more videos and slower pace. Thank you

por Alejandro O

15 de ene. de 2019

More hands on activities is the common theme on all classes, its a lot of talking and not a lot of putting things together, follow the University of Michigan Python curriculum, that one is great for hands on learning.

por Srinivasan D

16 de ago. de 2020

Many installations on the pylab notebooks are broken. There are version conflicts all the time. Even if I run the notebook with no changes of my own, several errors appear. This wastes a lot of time.

por willy k

19 de ago. de 2020

some issues on some labs due to OAuth compatibility ...

see

ERROR: witwidget 1.5.1 has requirement oauth2client>=4.1.3, but you'll have oauth2client 3.0.0 which is incompatible.

por Leszek Ś

13 de ago. de 2018

Please update instructions. UI has been changed.

Some code doesn't execute. Last lab. Should be updated. This can be just one sentence (simply, versions of packages don't fit).

por Dimitry I

12 de nov. de 2019

This wasn't a bad course, but it is more geared towards showcasing GCP features (BigQuery, Dataflow, Apache Beam, etc.) rather than teaching feature engineering.

por Franco G

5 de ene. de 2020

The course focuses much more on the gcp tools rather than the feature engineering, labs were not easy to follow, some pieces of code did not work properly.

por Alouini M Y

16 de sep. de 2018

A good course overall. However, the last two labs didn't run since packages couldn't be installed. Please update these labs. :)

por Yuan L

17 de abr. de 2021

Some lab notebooks need to be updated. Especially for week 4, some setup steps are missing. Otherwise, good content.

por Sandip K M

26 de nov. de 2019

Some of the Labs do not work and the information provided are not enough to debug the issue.

por Arturo M

20 de nov. de 2018

Too long for one week. I would suggest to split it in two or even three weeks

por Carlos B

20 de dic. de 2018

The work needed was waaaaay below a one week

por Matthew S

5 de ago. de 2018

Some missing steps in lab descriptions

por Xinyue Z

14 de sep. de 2018

Some labs don't work

por Cooper C

16 de ene. de 2020

I feel that this, and the tensor flow course that proceeds it in the specialization, were a waste of my time. My feeling is that this entire specialization is a glorified demonstration of what GCP can do with ML. The labs are not interactive and in some cases did not work. I don't feel that I have learned anything new. If I were to use GCP for ML purposes, I would need additional training to do it. I don't recommend this specialization.

por Alex H

21 de oct. de 2019

Great instructor but (1) the coding challenges are buggy and don't really teach you anything and (2) a lot of the material in this course is tedious for someone with professional training in AI but no experience with GCP

por Tulio C

10 de may. de 2020

The content is dense but taught superficially. The answers are given away and students have no time to explore the content. The lectures should be broken down into more weeks so that students can absorb the information.

por Bruno Z

29 de sep. de 2021

1. Some of the labs don't work (any more), require old versions of TFX or lack information.

2. Quizzes have blatantly obvious mistakes

The lectures and slides are good, though.

por A A

7 de nov. de 2019

the lectures are good, can be boring. The course would have been more interesting if it had thought-out assignments instead of demo-code to just run as labs

por Thibault D

14 de sep. de 2019

The gap between the lecture and the coding is too big. The coding sessions need to be more interactive to be useful.

por Marko H

6 de abr. de 2019

Basically this course would receive four stars, but repeated problems with qwiklabs had a severe impact on my overall experience. I got thrown out three times in a row (and my account locked) during dataflow lab.

Every time I had to request unlockin of my account, which took half a day every time. When requesting advice to avoid this error, I got offered the general and vague explanation that I "should only use the resources required by the lab". I am 100% sure that I didn't use any extra resources, including zones and regions.

The Coursera's helpdesk went behind the excuse that Qwiklabs is a third-party service. That may be the case, but since Qwiklabs has been integrated into the Courseras' course, the ultimate responsibility lies with Coursera.

I hope that Coursera will co-operate with Qwiklabs to sort out this very annoying problem.

por Nathan K

29 de oct. de 2018

Ultimately I found this course to be disappointing, because the Google APIs for DataFlow, BigQuery, etc. are unusable with the provided QuickLabs account. When you try to activate any API during the labs, it asks you for a location. It is a required field that says: "You must select a parent organization or folder." Clicking this option reveals a single organization called "no organization," which is not a legitimate choice. APIs cannot be activated and then cannot be used in the lab.

Because of this I was unable to actually do many of the labs that required the use of the Google APIs including the keystone lab "Improve ML model with Feature Engineering" where the taxi-fare prediction model is refined into a perfected state.

I'm upset that I paid money for this.

por Phillip

15 de ago. de 2020

The last three sections of this course are very difficult. I think the material needs to simplified, less prepositions, to much explanation not enough demonstration, use a thousand words to explain straight forward concepts makes the last part of this course impossible. If any one completes this section with a clear understanding of it's fundamentals, I wish they'd give me a call - frustration - aargh!

por Siew W O

20 de jun. de 2020

This module is interesting but unfortunately it is also plagued with problems. Two key issues that hopefully can be looked into. Firstly, there could be better explanation on Apache Beam. Secondly, I can't run quite a number Qwiklabs because modules not found or some simple import commands are missing

por john f d

18 de jul. de 2018

Labs vms are to slow. Speaker is difficult to understand. Mic varies and speech pattern is not clear. The presentations need some graphics rather than a guy talking. Sketch out the ideas on a white board rather than talking 5 minutes to a single slide.