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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

What's an algorithm? What's a model?

Before we dive into data munging, let's take a moment to explain the difference between an algorithm and a model, two terms we've been using up until now without a formal definition.

Consider the simple linear regression example we saw in Chapter 1, Introduction to Machine Learning and Predictive Analytics — the linear regression equation with one predictor:

Here, x is the variable, Å· the prediction, not the real value, and (a,b) the parameters of the linear regression model:

  • The conceptual or theoretical model is the representation of the data that is the most adapted to the actual dataset. It is chosen at the beginning by the data scientist. In this case, the conceptual model is the linear regression model, where the prediction is a linear combination of a variable. Other conceptual models include decision trees, naive bayes, neural networks...
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