To get the most out of this book

In this book, machine learning algorithms are implemented on the Google Cloud Platform. To reproduce the many examples in this book, you need to possess a working account on GCP. We have used Python 2.7 and above to build various applications. In that spirit, we have tried to keep all of the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Machine-Learning-on-Google-Cloud-Platform. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/HandsOnMachineLearningonGoogleCloudPlatform_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Where GROUP is a service or an account element and COMMAND is the command to send to the GROUP."

A block of code is set as follows:

import matplotlib.patches as patches
import numpy as np
fig,ax = plt.subplots(1)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

text="this is a good text"
from google.cloud.language_v1 import types
document = types.Document(
        content=text,
        type='PLAIN_TEXT')

Any command-line input or output is written as follows:

$ gcloud compute instances list

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Click on Create a new project."

Warnings or important notes appear like this.

Tips and tricks appear like this.