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Effective Amazon Machine Learning

Effective Amazon Machine Learning

By : Perrier
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Effective Amazon Machine Learning

Effective Amazon Machine Learning

By: Perrier

Overview of this book

Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets.
Table of Contents (10 chapters)
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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 Amazon ML to take all the dataset variables into account for model training will be as follows:

{
    "groups" : {},
    "assignments" : { },
    "outputs":["ALL_INPUTS"]
}

The recipe does not transform the data in any way.

Note

The...

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