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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

Going beyond classification and regression

Although Amazon ML is set to solve classification and regression problems, the service can also be used for other supervised data science problems. In this last section, we looked at two classic problems: Recommender systems and named entity recognition.

  • Making recommendations: A recommender system seeks to predict the rating or preference that a user would give to an item. There are several strategies to build recommender systems: 
  • Collaborative filtering: This involves using the behavioral patterns of similar users to predict a given user's preferences. It's the other people also bought this approach.
  • Content-based filtering: This is the strategy where the features of a certain content are used to group similar products or content. 

To use Amazon ML for recommendations, you can frame your solution as a content-based recommendation...

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