Effective Amazon Machine Learning

Learn to leverage Amazon's powerful platform for your predictive analytics needs

Effective Amazon Machine Learning

This ebook is included in a Mapt subscription
Alexis Perrier

Learn to leverage Amazon's powerful platform for your predictive analytics needs
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Book Details

ISBN 139781785883231
Paperback306 pages

Book Description

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

Chapter 1: Introduction to Machine Learning and Predictive Analytics
Introducing Amazon Machine Learning
Understanding predictive analytics
Diving further into linear modeling for prediction
Summary
Chapter 2: 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
Chapter 3: 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
Chapter 4: Loading and Preparing the Dataset
Working with datasets
Preparing the data
Creating the datasource
Examining data statistics 
Feature engineering with Athena
Summary
Chapter 5: Model Creation
Transforming data with recipes
Creating a model
Creating an evaluation
Analyzing the logs
Summary
Chapter 6: Predictions and Performances
Making batch predictions
Making real-time predictions
Summary
Chapter 7: Command Line and SDK
Getting started and setting up
A simple project using the CLI
Boto3, the Python SDK
Summary
Chapter 8: Creating Datasources from Redshift
Choosing between RDS and Redshift
Introducing polynomial regression
Polynomial regression in Amazon ML
Summary
Chapter 9: Building a Streaming Data Analysis Pipeline
Streaming Twitter sentiment analysis
Going beyond classification and regression
Summary

What You Will Learn

  • Learn how to use the Amazon Machine Learning service from scratch for predictive analytics 
  • Gain hands-on experience of key Data Science concepts
  • Solve classic regression and classification problems
  • Run projects programmatically via the command line and the python SDK 
  • Leverage the Amazon Web Service ecosystem to access extended data sources
  • Implement streaming and advanced projects

Authors

Table of Contents

Chapter 1: Introduction to Machine Learning and Predictive Analytics
Introducing Amazon Machine Learning
Understanding predictive analytics
Diving further into linear modeling for prediction
Summary
Chapter 2: 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
Chapter 3: 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
Chapter 4: Loading and Preparing the Dataset
Working with datasets
Preparing the data
Creating the datasource
Examining data statistics 
Feature engineering with Athena
Summary
Chapter 5: Model Creation
Transforming data with recipes
Creating a model
Creating an evaluation
Analyzing the logs
Summary
Chapter 6: Predictions and Performances
Making batch predictions
Making real-time predictions
Summary
Chapter 7: Command Line and SDK
Getting started and setting up
A simple project using the CLI
Boto3, the Python SDK
Summary
Chapter 8: Creating Datasources from Redshift
Choosing between RDS and Redshift
Introducing polynomial regression
Polynomial regression in Amazon ML
Summary
Chapter 9: Building a Streaming Data Analysis Pipeline
Streaming Twitter sentiment analysis
Going beyond classification and regression
Summary

Book Details

ISBN 139781785883231
Paperback306 pages
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