• Online, Self-Paced
Course Description

Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy. This 10-video course begins with an overview of machine learning anomaly detection techniques, by focusing on the supervised and unsupervised approaches of anomaly detection. Then learners compare the prominent anomaly detection algorithms, learning how to detect anomalies by using R, RCP, and the devtools package. Take a look at the components of general online anomaly detection systems and then explore the approaches of using time series and windowing to detect online or real-time anomalies. Examine prominent real-world use cases of anomaly detection, along with learning the steps and approaches adopted to handle the entire process. Learn how to use boxplot and scatter plot for anomaly detection. Look at the mathematical approach to anomaly detection and implementing anomaly detection using a K-means machine learning approach. Conclude your coursework with an exercise on implementing anomaly detection with visualization, cluster, and mathematical approaches.

Learning Objectives

Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy. This 10-video course begins with an overview of machine learning anomaly detection techniques, by focusing on the supervised and unsupervised approaches of anomaly detection. Then learners compare the prominent anomaly detection algorithms, learning how to detect anomalies by using R, RCP, and the devtools package. Take a look at the components of general online anomaly detection systems and then explore the approaches of using time series and windowing to detect online or real-time anomalies. Examine prominent real-world use cases of anomaly detection, along with learning the steps and approaches adopted to handle the entire process. Learn how to use boxplot and scatter plot for anomaly detection. Look at the mathematical approach to anomaly detection and implementing anomaly detection using a K-means machine learning approach. Conclude your coursework with an exercise on implementing anomaly detection with visualization, cluster, and mathematical approaches.

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Data Administration