Chevron Left
返回到 特色工程

学生对 Google 云端平台 提供的 特色工程 的评价和反馈

4.5
stars
1,236 个评分
120 条评论

课程概述

Want to know how you can improve the accuracy of your machine learning models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering on Google Cloud Platform where we will discuss the elements of good vs bad features and how you can preprocess and transform them for optimal use in your machine learning models. In this course you will get hands-on practice choosing features and preprocessing them inside of Google Cloud Platform with interactive labs. Our instructors will walk you through the code solutions which will also be made public for your reference as you work on your own future data science projects. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<...

热门审阅

OA

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.

BR

Jan 10, 2020

i really like the effort taken in developing this course, the structure. Kudos to Laks for converting lots of statistical and coding language to very simple understandable english.

筛选依据:

101 - 特色工程 的 120 个评论(共 120 个)

创建者 Alejandro O

Jan 15, 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.

创建者 Leszek Ś

Aug 13, 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).

创建者 Super-intelligent S o t C B

Nov 12, 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.

创建者 franco g

Jan 06, 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.

创建者 Alouini M Y

Sep 16, 2018

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

创建者 Sandip K M

Nov 26, 2019

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

创建者 Arturo M

Nov 20, 2018

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

创建者 Carlos B

Dec 20, 2018

The work needed was waaaaay below a one week

创建者 Matthew S

Aug 05, 2018

Some missing steps in lab descriptions

创建者 Xinyue Z

Sep 14, 2018

Some labs don't work

创建者 Cooper C

Jan 16, 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.

创建者 Alex H

Oct 21, 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

创建者 A A

Nov 08, 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

创建者 Thibault D

Sep 14, 2019

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

创建者 Marko H

Apr 06, 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.

创建者 Nathan K

Oct 29, 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.

创建者 john f d

Jul 18, 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.

创建者 ni_tempe

Oct 07, 2019

this is useless...google is advertising their product and making us pay for it. They should learn dr Andrew Ng and create courses which teach us without using a specific platform.

创建者 Arman A

Apr 11, 2019

Pros: Tensorflow is an excellent framework for deep learning

Cons :

1- The way this material is designed is 10 X SHIT

2- Either teach properly or don't teach at all.

创建者 yannick t

Jun 11, 2018

Not very clear + lack of real student practice