Standard 2 stops to get here · leads to 1
Anomaly Detection
Identifying unusual patterns or outliers in data that don't conform to expected behavior, used for fraud detection and monitoring.
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2 stops · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Unsupervised Learning ✓ understood
Learning from unlabeled data to discover hidden patterns, structures, or relationships without explicit target outputs.
- Anomaly Detection · you are here ✓ understood
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Foundations Fraud Detection Identifying fraudulent transactions or activities using anomaly detection and pattern recognition in financial data. Evaluation Out-of-Distribution Data that differs significantly from the training distribution, where models often perform poorly or unreliably. Foundations Clustering An unsupervised learning technique that groups similar data points together based on their features or characteristics. Neural Networks Autoencoder An unsupervised neural network that learns to compress data into a latent representation and reconstruct it, useful for dimensionality reduction. Shipping AI Data Drift Changes in input data distribution over time that can degrade model performance in production.