Chevron Left
Back to Feature Engineering

Learner Reviews & Feedback for Feature Engineering by Google Cloud

4.5
stars
1,628 ratings
179 reviews

About the Course

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....

Top reviews

GS
Apr 8, 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
Nov 25, 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.

Filter by:

126 - 150 of 179 Reviews for Feature Engineering

By Emily T

Jul 5, 2019

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

By Sandeep K

Jul 29, 2018

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

By Nagireddy S R

Dec 13, 2018

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

By borja v

Jun 21, 2019

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

By ThemisZ

Feb 4, 2020

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

By Alexander Z

Dec 29, 2018

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

By Marcos H

Nov 8, 2018

Very practical and Lak is a great teacher and communicator!

By Fernandes M R

May 15, 2020

Maybe a little more example of how deal with features.

By Malithi N

May 25, 2020

This course explains theories nicely with labs

By Joel M

Dec 6, 2018

good clear instructions, and valuable content.

By Anupam P

Aug 26, 2019

Comprehensive yet precise and clear.

By Rohit K A

Dec 24, 2018

No course material for reference

By Michael C

Nov 29, 2020

Very important information here

By Rahul K

May 5, 2019

Lovely Course. Thanks Google

By Ripunjoy G

Nov 21, 2019

Labs have problems

By Rohit K S

Sep 18, 2020

Interesting!!

By Abhishek S

Sep 21, 2020

very helpful

By Terry L

May 1, 2019

개요를 알게 되서 좋음

By Benjamin F

Apr 8, 2020

noice

By Ahmad T

Aug 27, 2019

Great

By Yingchuan H

Sep 16, 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.

By Fabrizio F

Aug 6, 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.

By Jonathan A

Aug 27, 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.

By irfan s p

Apr 10, 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

By 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.