This is part of Machine Learning-Driven Anomaly Detection
What you will learn
- Overview of popular anomaly detection algorithms for independent univariate data
- Details of running one of the algorithms on Spark
By independent we mean the data is not autoregressive. i.e the current values do not depend on past values. As we have seen the way we can determine if the data is independent is through autocorrelation. Data that is independent will only have a large peak at zero lag and zero or insignificant values for other lags. The algorithms used for detecting an anomaly in this kind of data is mostly based on statistical distribution, parametric, or non-parametric.
This is a FREE class!
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See below for past session recordings & notes
COO, CIO, DevOps, Software Engineers
- Must have: Development experience
- Nice to have: Python knowledge
What to Bring
- Please bring a reasonably modern laptop (Corporate laptops with overly restrictive firewalls may not work well; Personal laptops are recommended)
- [nice to have] download our docker image elephantscale/es-training
Pranab Ghosh is a Data Science Consultant, He owns several open-source Big Data and Data Science projects using Hadoop, Spark, Storm, Kafka, NoSQL databases, and the related ecosystem.