Effective Amazon Machine Learning

By : Perrier

Buy this Book

Effective Amazon Machine Learning

By: Perrier

Buy this Book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Introduction to Machine Learning and Predictive Analytics

Introducing Amazon Machine Learning

Understanding predictive analytics

Diving further into linear modeling for prediction

Summary

Machine Learning Definitions and Concepts

What's an algorithm? What's a model?

Dealing with messy data

The predictive analytics workflow

Identifying and correcting poor performances

Evaluating the performance of your model

Summary

Overview of an Amazon Machine Learning Workflow

Opening an Amazon Web Services Account

Setting up the account

Overview of a standard Amazon Machine Learning workflow

Summary

Loading and Preparing the Dataset

Working with datasets

Preparing the data

Creating the datasource

Examining data statistics

Feature engineering with Athena

Summary

Model Creation

Transforming data with recipes

Creating a model

Creating an evaluation

Analyzing the logs

Summary

Predictions and Performances

Making batch predictions

Making real-time predictions

Summary

Command Line and SDK

Getting started and setting up

A simple project using the CLI

Boto3, the Python SDK

Summary

Creating Datasources from Redshift

Choosing between RDS and Redshift

Introducing polynomial regression

Polynomial regression in Amazon ML

Summary

Building a Streaming Data Analysis Pipeline

Streaming Twitter sentiment analysis

Going beyond classification and regression

Summary

Customer Reviews

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Summary

In this chapter, we saw how to use Redshift as a datasource for Amazon ML. Although RDS could also have been used to create datasources, Redshift is much easier to use with Amazon ML as all the access configuration is taken care of by the AWS wizard.

We have shown how to use simple SQL queries on Redshift to carry out feature engineering and implement a polynomial regression approach on a highly non-linear dataset. We have also shown how to generate the required SQL queries, schemas, and recipes to carry out the Monte Carlo cross-validation.

In the next chapter, we will build on our Redshift integration and start streaming data using the AWS Kinesis service.