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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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Loading and Preparing the Dataset

Data preparation involves data cleaning and feature engineering. It is the most time-consuming part of a machine learning project. Amazon ML offers powerful features to transform and slice the data. In this chapter, we will create the datasources that Amazon ML requires to train and select models. Creating a datasource involves three steps:

Making the dataset available on AWS S3.
Informing Amazon ML about the nature of the data using the schema.
Transforming the initial dataset with recipes for feature engineering.

In a second part, we will extend Amazon ML data modification capabilities in order to carry out powerful feature engineering and data cleansing by using Amazon SQL service Athena. Athena is a serverless SQL-based query service perfectly suited for data munging in a predictive analytics context.