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

Summary


In this introductory chapter, we presented the techniques used by the Amazon ML service. Although Amazon ML offers fewer features than other machine learning workflows, Amazon ML is built on a solid ground, with a simple yet very efficient algorithm driving its predictions.

Amazon ML does not offer to solve any type of automated learning problems and will not be adequate in some contexts and some datasets. However, its simple approach and design will be sufficient for many predictive analytics projects, on the condition that the initial dataset is properly preprocessed and contains relevant signals on which predictions can be made.

In Chapter 2, Machine Learning Definitions and Concepts, we will dive further into techniques and concepts used in predictive analytics.

More precisely, we will present the most common techniques used to improve the quality of raw data; we will spot and correct common anomalies within a dataset; we will learn how to train and validate a predictive model and how to improve the predictions when faced with poor predictive performance.