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Effective Amazon Machine Learning

You're reading from   Effective Amazon Machine Learning Expert web services for machine learning on cloud

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781785883231
Length 306 pages
Edition 1st Edition
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Author (1):
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Alexis Perrier Alexis Perrier
Author Profile Icon Alexis Perrier
Alexis Perrier
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to Machine Learning and Predictive Analytics FREE CHAPTER 2. Machine Learning Definitions and Concepts 3. Overview of an Amazon Machine Learning Workflow 4. Loading and Preparing the Dataset 5. Model Creation 6. Predictions and Performances 7. Command Line and SDK 8. Creating Datasources from Redshift 9. Building a Streaming Data Analysis Pipeline

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Set original weight estimation at w_0 = 100g to initialize and a counter."

A block of code is set as follows:

# Create datasource for training
resource = name_id_generation('DS', 'training', trial)
print("Creating datasources for training (%s)"% resource['Name'] )

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

$ aws s3 cp data/titanic.csv s3://aml.packt/data/ch9/

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Examples of reinforcement learning applications include AlphaGo, Google's world championship Go algorithm, self-driving cars, and semi-autonomous robots."

Warnings or important notes appear in a box like this.
Tips and tricks appear like this.
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