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

Streaming Twitter sentiment analysis


In this chapter, our main project consists of real-time sentiment classification of Tweets. This will allow us to demonstrate how to use an Amazon ML model that we've trained to process real-time data streams, by leveraging the AWS data ecosystem.

We will build an infrastructure of AWS services that includes the following:

  • Amazon ML: to provide a real-time classification endpoint
  • Kinesis firehose: To collect the Tweets
  • AWS Lambda: To call an Amazon ML streaming endpoint
  • Redshift: To store the Tweets and their sentiment
  • S3: To act as a temporary store for the Tweets collected by Kinesis Firehose
  • AWS Cloudwatch: To debug and monitor

We will also write the necessary Python scripts that feed the Tweets to Kinesis Firehose.

Popularity contest on twitter

All good data science projects start with a question. We wanted a social network question not tied to a current political or societal context. We will be looking into the popularity of vegetables on twitter. We want...