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Introduction

The course is part of these learning paths

Machine Learning on Google Cloud Platform
course-steps 2 certification 1

Contents

keyboard_tab
Introduction
Training Your First Neural Network
3
Improving Accuracy
Conclusion
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Introduction
Overview
Transcript
DifficultyIntermediate
Duration55m
Students1116
Ratings
4.9/5
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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

Resources

Updates

  • Nov. 16, 2018: Updated 90% of the lessons due to major changes in TensorFlow and Google Cloud ML Engine. All of the demos and code walkthroughs were completely redone.

About the Author

Students14603
Courses41
Learning paths21

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).

Welcome to the “Introduction to Google Cloud Machine Learning Engine”. I’m Guy Hummel and I’ll be showing you how to build and run neural networks on the Google Cloud Platform.

 

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 go through the Getting Started guide with ease.

 

To get the most from this course, you should have some experience writing Python code.

 

This is a hands-on course with lots of demonstrations. The best way to learn is by doing, so I recommend that you try performing these tasks yourself on your own Google Cloud account.  If you don’t have one, then you can sign up for a free trial.

 

To train your first neural network, we’ll start by going over machine learning concepts. Then, we’ll go through a TensorFlow program and run it. TensorFlow is a set of Python libraries that make it easier to create neural networks. Google open sourced it in 2015.

 

Next, you’ll learn about deep neural networks, also known as deep learning, and then use Google’s ML Engine to train your machine learning model.

 

To see how to improve the accuracy of models, we’ll use feature engineering and then combine two different types of models.

 

After that, we’ll scale up by training a model using a distributed cluster on ML Engine, and then deploy the trained model, so we can use it to make predictions.

 

If you’re ready to learn how to train a machine learning model on the Google Cloud Platform, then let’s get started.