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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

Transforming data with recipes

A crucial element of the data science workflow is feature engineering. Amazon ML offers certain data transformations via its data recipes. Note that although transformations are conceptually part of the ETL or data preparation phase of a predictive analytics workflow, in Amazon ML, data recipes are part of the model-building step and not of the initial datasource creation step. In this section, we start by reviewing the available data transformations in Amazon ML, and then we apply some of them to the Titanic dataset using the Titanic train set 11 variables datasource.

Managing variables

Recipes are JSON-structured scripts that contains the following three sections in the given order:

  • Groups
  • Assignments
  • Outputs

An empty recipe instructing...

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