Launching into 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

Created by: Google Cloud Training

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Overall Score : 88 / 100

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Course Description

Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.Course Objectives:Identify why deep learning is currently popularOptimize and evaluate models using loss functions and performance metricsMitigate common problems that arise in machine learningCreate repeatable and scalable training, evaluation, and test datasetsCOMPLETION CHALLENGEComplete any GCP specialization from November 5 - November 30, 2019 for an opportunity to receive a GCP t-shirt (while supplies last). Check Discussion Forums for details.

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Instructor Details

Google Cloud Training

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.

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Reviews

4.4

147 total reviews

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By Raghuram N on 27-Apr-19

Great course. Gradient descent and loss function concepts were explained well.

By Dirk K on 24-Aug-18

The videos are ok, the "Labs" are really bad. You just follow instructions with code to copy into the notebook. Of course, you can play a bit with the code, but you don't really learn how to do it yourself when the correct answer is already filled in. Would not recommend.

By Raja R G on 5-Dec-18

Thanks...

By pankaj b g on 3-Dec-18

Good learning path

By SUJITH V on 4-Dec-18

A great course to boost your confidence on practicing ML. It also teaches you some fresh skills like repeatable dataset partitioning techniques using just SQL.

By Shayne C on 9-Dec-18

Super fun course.

By rohit k s on 31-Dec-18

Another great learning experience and has given me the confidence to keep moving forward.

By shabeer m on 5-Feb-19

fanstatic course

By BHARATHI A on 5-Feb-19

5 starz - very good info and pointful.

By Joel M on 20-Nov-18

Good clear course with strong incremental learning.

By Mark B on 7-Nov-18

Great lectures and labs thanks. The first lecture block made a lot of great connections between topics and methods past and present yet get the most out of it, one ideally has recently reviewed the theory behind the tradition tools. Otherwise the first block is a bit of a drink from the firehose although one can still pick up the gist message but may not get some of the other enriching points. In any case. Great work and thanks

By Zezhou J on 7-Nov-18

I love the course introducing core concepts and practices in machine learning today as well as some historical development. This course feels more rigorous because some core mathematical foundations are introduced. I kind of hope there could be more theoretical explanation in more depths with some references attached.