Book Image

Effective Amazon Machine Learning

By : Alexis Perrier
Book Image

Effective Amazon Machine Learning

By: Alexis 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 (17 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface

Summary


In this chapter, we focused on what is commonly known as the Extract Load Transform (ETL) part of the data science flow with regard to the Amazon ML service. We saw that the Amazon ML datasource is a set of information comprised of location, data structure, and data analytics given to the service so that it can use that data to start training models. You should now feel comfortable creating an Amazon ML datasource from an original CSV data file made accessible via S3. 

We have also explored ways to transform the data and create new features via the AWS Athena service using simple SQL queries. The ability to complement the features of Amazon ML by leveraging the AWS ecosystem is one of the main benefits of using Amazon ML.

We now have a couple of Titanic datasets, the original one and the extended one, which are split into training and held-out subsets, and we have created the associated datasources.

In Chapter 5, Model Creation, we will use these datasets to train models, and we will...