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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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Polynomial regression in Amazon ML

We will use Boto3 and Python SDK and follow the same method of generating the parameters for datasources that we used in Chapter 7, Command Line and SDK, to do the Monte Carlo validation: we will generate features corresponding to power 2 of x to power P of x and run N Monte Carlo cross-validation. The pseudo-code is as follows:

for each power from 2 to P:
write sql that extracts power 1 to P from the nonlinear table
do N times
Create training and evaluation datasource
Create model
Evaluate model
Get evaluation result
Delete datasource and model
Average results

In this exercise, we will go from 2 to 5 powers of x and do 5 trials for each model. The Python code to create a datasource from Redshift using create_data_source_from_rds() is as follows:

response = client.create_data_source_from_redshift(
DataSourceId...

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