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

Chapter 2. Machine Learning Definitions and Concepts

This chapter offers a high-level definition and explanation of the machine learning concepts needed to use the Amazon Machine Learning (Amazon ML) service and fully understand how it works. The chapter has three specific goals:

  • Listing the main techniques to improve the quality of predictions used when dealing with raw data. You will learn how to deal with the most common types of data problems. Some of these techniques are available in Amazon ML, while others aren't.
  • Presenting the predictive analytics workflow and introducing the concept of cross validation or how to split your data to train and test your models.
  • Showing how to detect poor performance of your model and presenting strategies to improve these performances.

The reader will learn the following:

  • How to spot common problems and anomalies within a given dataset
  • How to extract the most information out of a dataset in order to build robust models
  • How to detect and improve upon poor...