[Book Discussion] - Practical Machine Learning: A New Look At Anomaly Detection

Discussion created by maprcommunity Employee on Nov 10, 2016



Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. In this ebook, two committers of the Apache Mahout project use practical examples to explain how the underlying concepts of anomaly detection work.
From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data.



The concepts described in this ebook will help you tackle anomaly detection in your own project.
- Use probabilistic models to predict what’s normal and contrast that with what you observe
- Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm
- Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probabilistic model
- Use historical data to discover anomalies in sporadic event streams, such as web traffic
- Learn how to use deviations in expected behavior to trigger fraud alert



Add a comment below.  What did you think about this book?, What was your favorite part?, What would you like to read next about?

Ellen Friedman andTed Dunning are eager to know your thoughts.


Haven't read the book yet?, find it here Practical Machine Learning: A New Look at Anomaly Detection | MapR 

See more available books in the  Converge Book Club