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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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Creating a model


Amazon ML always suggests a recipe based on your datasource when you create a model. You can choose to use that recipe or to modify it. We will now create our first model and during that process analyze the recipe Amazon ML has generated for us.

Go to the model dashboard, and click on the Create new... | ML model button.

You will go through three screens:

  1. Select the datasource, choose the Titanic train set with 11 variables.
  2. Amazon ML will validate the datasource and present a summary.
  3. Choose the default or Custom model creation; choose the custom path:

The next screen is split between the attributes, their type and a sample of values on the left side, and the suggested recipe on the right side, as shown in the following screenshot:

Editing the suggested recipe

This is where you can edit the recipe and replace it with a recipe of your own creation.

Note

You can find all the JSON in this chapter in the book's GitHub repository, properly formatted and indented at https://github.com...

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