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Practical Machine Learning: A New Look at Anomaly Detection

Ted Dunning, Ellen Friedman




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 | #1580416 in Books |  2014-09-06 | Original language:English | PDF # 1 |  9.02 x.14 x5.98l,.0 | File type: PDF | 66 pages

||1 of 1 people found the following review helpful.| Lightweight, disapointing|By C. Roeder|I came to the author and book by a personal recommendation and found, like the other review suggested, it's pretty light-weight. Light weight enough that you can do as well, or better, surfing the internet for this stuff. A book should spare you the work of finding and evaluating sources. I didn't connect well enough with this book to thin|About the Author|
|Ted Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects and mentor for these Apache projects: Spark, Storm, Stratosphere, and Datafu. H

Finding Data Anomalies You Didn't Know to Look For

Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what “suspects” you’re looking for. This O’Reilly report uses practical examples to explain how the underlying concepts of anomaly detection ...

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