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Volver a Ingeniería de características

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

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Acerca del Curso

Want to know about Vertex AI Feature Store? 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 discuss good versus bad features and how you can preprocess and transform them for optimal use in your models. This course includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow....

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 190 revisiones para Ingeniería de características

por Rohit K S

18 de sep. de 2020

Interesting!!

por Abhishek S

21 de sep. de 2020

very helpful

por Terry L

1 de may. de 2019

개요를 알게 되서 좋음

por Benjamin F

8 de abr. de 2020

noice

por Ahmad T

27 de ago. de 2019

Great

por Yingchuan H

16 de sep. de 2018

The content of this course might be a bit too much for one week compared to previous courses in the specialization. Also, it would be great if some of the labs are more clarified and introduce more opportunities for students to participate in writing code for the lab session rather than just going through it and running existing code. I did experience some issues installing the tf transform package for the last lab, which might not be a common issue, but was kind of frustrating as it prevents me from more exploration of the learned skills. Thanks for providing the course anyway. I learned a lot from it.

por Fabrizio F

6 de ago. de 2018

The subject is very interesting and I was alwyas curious about how Feature Engineering should be done with Tensorflow. I come from Pandas, where feature engineering is not that difficult, but with Tensorflow it is different and not that intuitive. Here in the course three different ways are presented. I guess I'll have to study more Apache Beam.

por Jonathan A

27 de ago. de 2018

The concepts were taught well. However, a lot of code and cloud interaction was involved, making the labs a key piece of the material. Two of the labs didn't work because the Google lectures aren't up-to-date with the Google APIs. Although Coursera response to the bad labs was prompt, the Google team did not respond.

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 Marcelo M C

17 de jun. de 2022

It started very well, but in the end it got a little messy, difficult to follow, lost fluency of the concepts being explained and complex to understand the different technologies and how they compare and how should be applied.

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