How Google does Machine Learning

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

( 115 Reviews )

Created by: Google Cloud Training

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Overall Score : 98 / 100

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Course Description

What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.>>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<

Time: 10h

Instructor Details

Google Cloud Training

4.9 Rating
115 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

4.9

115 total reviews

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By Kevin C on 26-Jun-18

This is too obviously an advertisement for google services disguised as a course.

By Yves G on 3-Jul-18

The omnipresent Google branding on software used is truly sufficient publicity in an educational setting, the branded t-shirts and multiple statements are superfluous for a Coursera class. Many statements, especially towards the first videos, create an impression that the technology isn't superior enough to sell itself as we learn it, but requires a sales pitch even though we already subscribed to the specialization on that particular Machine Learning software.

By Girish S K on 15-Jul-19

As an overview this course is OK. But the quality of labs can be improved.

By Akshat B on 18-Jul-19

This course lays the foundation of Machine Learning.Some assignments were not quite useful as it involved just running the programs

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 Peter H on 23-Jul-18