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Volver a Ingeniería de características

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

1,677 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

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.

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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126 - 150 de 183 revisiones para Ingeniería de características

por Attila B

8 de dic. de 2018

Really comprehensive course.Was a bit tough to follow sometimes,but guess it's just beginners problem.

por RIJO R

8 de sep. de 2020

Good Course, helped me to learn something new about GCS and feature learning. Thanks for this course.

por Emily T

5 de jul. de 2019

This course really needs more hands on work with code, but it was still good and I learned lots.

por Sandeep K

29 de jul. de 2018

this was really good, except removed one start for trifacta integration of dataflow lab.

por Nagireddy S R

13 de dic. de 2018

Felt like it was cut short at the end. Would like to see a bit more on the tf.transform

por borja v

21 de jun. de 2019

the course needs some code upgrades because of ML engine is close to be depecreated

por ThemisZ

4 de feb. de 2020

very nice course , -1 star for no pdf/ppt notes made available

por Alexander Z

29 de dic. de 2018

great content and cool notebooks ... sometimes hard to follow

por Marcos H

8 de nov. de 2018

Very practical and Lak is a great teacher and communicator!

por Fernandes M R

15 de may. de 2020

Maybe a little more example of how deal with features.

por Malithi N

25 de may. de 2020

This course explains theories nicely with labs

por Joel M

6 de dic. de 2018

good clear instructions, and valuable content.

por Anupam P

26 de ago. de 2019

Comprehensive yet precise and clear.

por Rohit K A

24 de dic. de 2018

No course material for reference

por Michael C

29 de nov. de 2020

Very important information here

por Rahul K

5 de may. de 2019

Lovely Course. Thanks Google

por Ripunjoy G

21 de nov. de 2019

Labs have problems

por Rohit K S

18 de sep. de 2020


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


por Ahmad T

27 de ago. de 2019


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.