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学生对 Google 云端平台 提供的 特色工程 的评价和反馈

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169 条评论


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....



Apr 09, 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.


Nov 26, 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.


76 - 特色工程 的 100 个评论(共 169 个)

创建者 Md A A M

Jul 04, 2020

Really Helpful.

创建者 LUU Q V

May 06, 2020

Great content!

创建者 Stephen Y

Apr 06, 2019

Great teaching

创建者 R M

Jun 19, 2020

Good Learning

创建者 Sofoniyas T D

May 01, 2020

Great indeed!

创建者 Eric

Feb 02, 2019

Learned a lot

创建者 Abdul R Y

Nov 28, 2018

Great Course!

创建者 Serhan A

Jun 05, 2018

Great series!

创建者 Phạm V T

Apr 17, 2020

Great course

创建者 Filbert K

May 01, 2019

Thank you.

创建者 Woojin J

Apr 30, 2019

nice & fun

创建者 Nayanajith P

May 28, 2019

It's nice

创建者 Kamlesh C

Jun 13, 2020


创建者 영신 박

Apr 29, 2019


创建者 Toby H

Aug 18, 2018

Love it.

创建者 Balasubramanian T K

Apr 13, 2020


创建者 Bielushkin M

Nov 16, 2018


创建者 Said A

Mar 22, 2019

A separate course to emphasise the role and importance of feature engineering in machine learning is what really got to me. With examples and explanation how your model can improve with feature engineering did the trick. Before, it was just a note in my notebook. Now, I really understand the importance of it.

However, having said that, the course could have been much shorter. It feels like, these courses are Google way of promoting its ML Cloud services.

创建者 Yaron K

Jul 21, 2018

Feature Engineering is critical. The course attempts to explain both the principles of feature engineering and it's implementation on Google cloud in a few hours - and as a result both are short-changed. Note also that unlike other courses on Coursera you can't audit this course, can't download videos and some of the most insightful videos don't even have subtitles.

创建者 Evren G

Nov 03, 2018

As ever excellent course content, the major let down and loss of star is because of the labs. There are no graded lab exercises where you have to think about and apply your theoretical learnings. Instead you get python notebooks that have completely prepopulated code. So the only thing you need to do is run the cells. A missed opportunity for excellent learning.

创建者 Michal K

Aug 19, 2018

In general, this course is very well prepared, covers a good piece of material and I'm leaving it with a lot of new things to try. One thing I would correct in the future: more coding. Don't get me wrong, labs are quite good in terms of examples quality, but since everything is already there, it is difficult to "learn by doing".

创建者 Sanket N

Apr 24, 2020

Course is explain feature engineering in such a way that is it is to understand what is purpose of it and also how to use it. Very good course.

One issue I found that, big query in second last model is not working. I have to search and fix issue in big query to run it. It is kind of distracting from main objective of Lab


创建者 Francois R

Apr 17, 2019

Very interesting theory, shows the power of Tensorflow in the field. I had trouble with the last lab though, which when I ran it step by step, would block my qwiklab account because of resource limitations...

创建者 Joe L

Nov 12, 2018

Great topics, the instructions are great. The only suggestion is cut down the number of different videos. just combine them together. 15 3 mins is not a great experience vs 3 15 min videos.

创建者 Timothy L

Aug 21, 2018

Some of the labs had big query errors, and some of the google cloud interfaces changed, so careful when doing the labs, the options and the buttons may have been shifted or renamed