Feature Engineering

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to feature

( 133 Reviews )

Created by: Google Cloud Training

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Overall Score : 80 / 100

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Course Description

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 <<

Time: 11h

Instructor Details

Google Cloud Training

4.0 Rating
133 Reviews

The Google Cloud Training team is responsible for developing, delivering and evaluating training that enables our enterprise customers and partners to use our products and solution offerings in an effective and impactful way. Google Cloud helps millions of organizations empower their employees, serve their customers, and build what's next for their businesses with innovative technology created in-and for-the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.

Reviews

4.0

133 total reviews

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By Stephen R on 27-Aug-18

A lot of the code, did not work.

By Yasim K on 12-Sep-18

The tf.transform and Apache Beam concepts are not explained in simple ways. Also the lab jumped from simple programs to complex programs.

By Martin A K on 31-May-19

Would appreciate more guidance on the exercises

By Sudesh A on 28-Jul-18

The videos are good and better than the last two courses in the specialization; however, the labs lack proper instructions and not that helpful. This course seems like more of an advertisement for Google Cloud Platform than feature engineering: details of engineering part is hardly covered in the course; more emphasis is on demonstrating on how to do it on GCP.

By Ian M on 27-Jul-18

Had a lot issues with the quiz grader.

By Mike W on 22-Jun-19

The notebook based demos are unfortunately pretty useless as labs. All of these courses would be much improved with real labs that require the student to build the system.

By Robert U on 11-Jun-19

The assessments do not actually require writing any code; you just execute the given code blocks. Little knowledge will be retained unless students actually write code and solve problems, even for the motivated ones who read through all the given code.

By Omar M A on 26-Nov-18

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.

By Abdul R Y on 28-Nov-18

Great Course!

By Mark B on 26-Dec-18

Very useful. Good job.Hands-on parts could be broken into smaller chunks with longer de-briefs.

By Mario R on 13-Jan-19

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.

By Putcha L N R on 13-Jan-19

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

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