Book Image

IBM Watson Projects

By : James D. Miller
Book Image

IBM Watson Projects

By: James D. Miller

Overview of this book

IBM Watson provides fast, intelligent insight in ways that the human brain simply can't match. Through eight varied projects, this book will help you explore the computing and analytical capabilities of IBM Watson. The book begins by refreshing your knowledge of IBM Watson's basic data preparation capabilities, such as adding and exploring data to prepare it for being applied to models. The projects covered in this book can be developed for different industries, including banking, healthcare, media, and security. These projects will enable you to develop an AI mindset and guide you in developing smart data-driven projects, including automating supply chains, analyzing sentiment in social media datasets, and developing personalized recommendations. By the end of this book, you'll have learned how to develop solutions for process automation, and you'll be able to make better data-driven decisions to deliver an excellent customer experience.
Table of Contents (12 chapters)

Defining the problem

In data mining, anomaly detection (or outlier detection) is defined as the identification of items, events, or observations that do not conform to an expected pattern (or other items) in a dataset, and that are sometimes referred to as rare events. These events raise suspicion and, typically, the anomalous items will translate to some kind of problem that requires deeper attention and needs to be addressed. Common events include bank fraud, structural defects, medical conditions, or simply mistakes in a text.

Anomalous items that raise suspicions by differing significantly from the majority of the data may also be referred to as outliers, novelties, noise, deviations, and exceptions.

Anomaly detection is a technique or method that is used to identify unusual patterns that don't seem to conform to the accepted behavior. You will routinely see anomaly...