5, 10 or 20 seats+ for your team - learn more
Failure is not an option for Sigma Corp. As a lead data scientist for the large conglomerate of energy production companies, it’s up to you to help ensure interruption-free operations by developing a means for detecting anomalies that signal potential problems. Using metrics, including the receiver operating characteristic (ROC) curve and the area under curve (AUC) score, you’ll evaluate anomaly detection algorithms. You’ll build a z-score anomaly detection algorithm, which focuses on a single feature and provides a simple benchmark, and you’ll apply it to a dataset to establish a reference for comparison. When you’re finished, you’ll have a firm grasp of z-score anomaly detection, classification error categories, and evaluating anomaly detection algorithms.
This liveProject is for beginner data scientists interested in learning the sought-after skills of building, implementing, and evaluating anomaly detection algorithms. To begin these liveProjects you’ll need to be familiar with the following:TOOLS
In this liveProject, you’ll learn to build, implement, and evaluate anomaly detection algorithms.
geekle is based on a wordle clone.