Anomaly Detection Session 4: Multivariate data anomaly detection on Python

Date
November 20, 2020
Start Time
9:00 AM PST
End Time
10:00 AM PST
Register for this Event

Overview

This is part of Machine Learning-Driven Anomaly Detection

What you will learn

The data is multivariate  with significant cross-correlation but without significant autocorrelation  or not sequential data at all

Will be discussing

  • Nearest Neighbour
  • Local Outlier Factor
  • Isolation Forest
  • Clustering 
  • Dimension Reduction
  • Auto Encoder

This is a FREE class!

On-Demand

Can’t make it to the live session?  No worries.  Go ahead and register; we will send you the session recording.
See below for past session recordings & notes

Intended Audience

COO, CIO, DevOps, Software Engineers

Prerequisites

  • 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
  • Class Notes here

Session Recording/Class Notes/Git Repo

Class Notes:

https://docs.google.com/document/d/16SxeSIbZgEZNFMphatNap5h3YRxFZDHtpeS56dAio1A/edit

Git Repo:

The python implementation is available in the open source  project avenir in GitHub

Presenter

Pranab Ghosh

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.

Linkedin: https://www.linkedin.com/in/pkghosh/