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  5. Introduction to Google Cloud Machine Learning Engine

Introduction

The course is part of these learning paths

Machine Learning on Google Cloud Platform

course-steps 2 certification 1

Data Engineer – Professional Certification Preparation for Google

course-steps 9 quiz-steps 5

Contents

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Introduction
1
Introduction1m 30s
Training Your First Neural Network
2
Machine Learning Concepts4m 34s
3
TensorFlow11m 3s
4
Deep Neural Networks3m 8s
5
Training a Model with ML Engine5m 15s
Improving Accuracy
6
Feature Engineering11m 25s
7
A Wide and Deep Model7m 13s
Scaling Up with ML Engine
8
Distributed Training on ML Engine12m 28s
9
Deploying a Model on ML Engine6m 46s
Conclusion
10
Conclusion31s
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Overview
Transcript
DifficultyIntermediate
Duration1h 4m
Students671

Description

Machine learning is a hot topic these days and Google has been one of the biggest newsmakers. Recently, Google’s AlphaGo program beat the world’s No. 1 ranked Go player. That’s impressive, but Google’s machine learning is being used behind the scenes every day by millions of people. When you search for an image on the web or use Google Translate on foreign language text or use voice dictation on your Android phone, you’re using machine learning. Now Google has launched Cloud Machine Learning Engine to give its customers the power to train their own neural networks.

If you look in Google’s documentation for Cloud Machine Learning Engine, you’ll find a Getting Started guide. It gives a walkthrough of the various things you can do with ML Engine, but it says that you should already have experience with machine learning and TensorFlow first. Those are two very advanced subjects, which normally take a long time to learn, but I’m going to give you enough of an overview that you’ll be able to train and deploy machine learning models using ML Engine.

This is a hands-on course where you can follow along with the demos using your own Google Cloud account or a trial account.

Learning Objectives

  • Describe how an artificial neural network functions
  • Run a simple TensorFlow program
  • Train a model using a distributed cluster on Cloud ML Engine
  • Increase prediction accuracy using feature engineering and both wide and deep networks
  • Deploy a trained model on Cloud ML Engine to make predictions with new data

 

About the Author

Students6940
Courses21
Learning paths9

Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).