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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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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

Overview of an Amazon Machine Learning Workflow

This chapter offers an overview of the workflow of a simple Amazon Machine Learning (Amazon ML) project, which comprises three main phases:

  1. Preparing the data
  2. Training and selecting the model
  3. Making predictions

The reader will learn how to get started on the Amazon Machine Learning platform, how to set up an account, and how to secure it. In the second part, we go through a simple numeric prediction problem based on a classic dataset. We describe each of the three steps mentioned above, what happens, what to expect, and how to interpret the final result.

In this chapter, we will study the following:

  • Opening an Amazon Web Services (AWS) account
  • Setting up the account
  • Overview of a standard Amazon Machine Learning workflow
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