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Feature Engineering, Google Cloud

4.4
485 個評分
60 個審閱

課程信息

>>> 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 <<< 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....

熱門審閱

創建者 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.

創建者 CV

Jul 01, 2018

Excellent Course and advice from experts about Feature Engineering and data pipelines utilizing advanced processes on GCP, thanks to Google and Coursera.

篩選依據:

60 個審閱

創建者 Nagireddy Srichakradhar Reddy

Dec 13, 2018

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

創建者 Attila Barus

Dec 08, 2018

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

創建者 Raja Ranjith Garikapati

Dec 07, 2018

Learned lots of stuff on feature engineering

創建者 Joel Mitchell

Dec 06, 2018

good clear instructions, and valuable content.

創建者 Abdul Rehman Yousaf

Nov 28, 2018

Great Course!

創建者 Omar Mohamed Amin

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.

創建者 Arturo Martin

Nov 20, 2018

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

創建者 Bielushkin Maksym

Nov 16, 2018

super

創建者 Joe Larson

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.

創建者 Zezhou Jing

Nov 09, 2018

The content is quite rich in this course. I feel decomposing it into two weeks might make it structurally more clear.