Professional Certificate in Outlier Detection
-- ViewingNowThe Professional Certificate in Outlier Detection is a comprehensive course that focuses on teaching learners how to identify and handle unusual data points, which is a critical skill in today's data-driven world. This course is essential for professionals working with large data sets, including data scientists, analysts, and researchers.
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• Introduction to Outlier Detection
• Understanding Data Normalization and Standardization
• Distance Measures for Outlier Analysis
• Types of Outliers: Point, Contextual, and Collective
• Unsupervised Outlier Detection Algorithms:
- Local Outlier Factor (LOF)
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
• Supervised Outlier Detection Algorithms:
- One-Class SVM (Support Vector Machine)
- Isolation Forest
• Semi-supervised Outlier Detection Algorithms:
- COP (Cluster-based Local Outlier Factor)
• Evaluation Metrics for Outlier Detection
• Real-world Applications and Case Studies in Outlier Detection
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- BasicUnderstandingSubject
- ProficiencyEnglish
- ComputerInternetAccess
- BasicComputerSkills
- DedicationCompleteCourse
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- NotAccreditedRecognized
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- ThreeFourHoursPerWeek
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