Imbalanced Data & Anomaly Detection ★★★ Expert Level
Imbalanced data and anomaly detection is not an easy problem to solve, especially when your project begins with imperfect data. This two-day training will teach you two effective and proven techniques to overcome these data issues and strengthen the success of all your data-based projects.
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Imbalanced Data & Anomaly Detection
- Point, contextual, and collective anomalies
- Univariate and multivariate anomalies
- Structured and unstructured anomaly detection
- Graphical tools such as jitter plots, violin plots, Z-scores and more
- Cost-sensitive learning
- Static and dynamic anomaly detection
- Z-score, modified Z-score, decomposition and forecasting for anomaly detection
- Two unsupervised algorithms: Isolation Forest, One-Class SVM
- Introduction to anomaly detection: Able to explain the context of anomalies and distinguish between different types.
- Graphical anomaly detection: Able to detect anomalies using data plotting.
- Handling imbalanced datasets: Able to maximize model results from imbalanced datasets.
- Unsupervised anomaly detection: Knowing when unsupervised learning can be applied and how to evaluate an unsupervised model.
- Time-series anomaly detection: Able to detect anomalies in time-series data.
- Unsupervised algorithms: Able to apply two unsupervised algorithms to detect anomalies.
- Detecting fraud using all anomaly detection tools: Able to combine all your knowledge to detect anomalies in a real-life case.