Course details

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/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.



Expected Duration (hours)
0.9

Lesson Objectives

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

  • Course Overview
  • describe the supervised and unsupervised approaches of anomaly detection
  • compare the prominent anomaly detection algorithms
  • demonstrate how to detect anomalies using R, RCP, and the devtools package
  • identify components of general online anomaly detection systems
  • describe the approaches of using time series and windowing to detect anomalies
  • recognize the real-world use cases of anomaly detection as well as the steps and approaches adopted to handle the entire process
  • demonstrate detecting anomalies using boxplot and scatter plot
  • demonstrate the mathematical approaches of detecting anomalies
  • implement anomaly detection using a K-means machine learning approach
  • implement anomaly detection with visualization, cluster, and mathematical approaches
  • Course Number:
    it_dsdiavdj_02_enus

    Expertise Level
    Intermediate