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Вернуться к Проектирование признаков

Проектирование признаков, Google Cloud

Оценки: 607
Рецензии: 70

Об этом курсе

>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: <<< 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....

Лучшие рецензии

автор: 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.

автор: 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.

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Рецензии: 70

автор: Eric

Feb 02, 2019

Learned a lot

автор: Gregory R. Gray Jr.

Jan 30, 2019

Thumbs sideways.

I learned a ton but it appears as technology grows and changes updates to the platform is sort of static.

автор: Sulthan Mohaideen N.K

Jan 18, 2019

had a great experience

автор: Alejandro Otanez

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.

автор: Putcha Lakshmi Narasimha Rahul

Jan 13, 2019

Amazing course! Gives insight into one of the most important part of solving Machine Learning problems; feature engineering!!

автор: Mario Rivas

Jan 13, 2019

This course should be mandatory for any ML practitioner. It teaches you that ML is not only about throwing whatever you want to (sort of) a model and expect to get reasonable results. It is about getting to know your problem and squeeze the data available.

автор: Alexander Zehetmaier

Dec 29, 2018

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

автор: Mark Broich

Dec 26, 2018

Very useful. Good job.

Hands-on parts could be broken into smaller chunks with longer de-briefs.

автор: Rohit Kumar Agarwal

Dec 24, 2018

No course material for reference

автор: Carlos Bravo

Dec 20, 2018

The work needed was waaaaay below a one week