Course Description
Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy.
Learning Objectives
Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools
- 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