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 chapter, we focused on two important elements of a predictive analytics project: the data and the evaluation of the predictive power of the model. We first listed the most common problems encountered with raw data, their impact on the linear regression model, and ways to solve them. The reader should now be able to identify and deal with missing values, outliers, imbalanced datasets, and normalization.

We also introduced the two most frequent problems in predictive analytics: underfitting and overfitting. L1 and L2 regularization is an important element in the Amazon ML platform, which helps overcome overfitting and make models more robust and able to handle previously unseen data.

We are now ready to dive into the Amazon Machine Learning platform in the next chapter.