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Phase 02: ML Fundamentals

AI From Scratch/Lesson 16/~75 minutes

Anomaly Detection

Normal is easy to define. Abnormal is whatever doesn't fit.

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Back to phaseML Fundamentals Next lessonHandling Imbalanced Data

Phase 02

ML Fundamentals

01What Is Machine Learning 02Linear Regression 03Logistic Regression 04Decision Trees and Random Forests 05Support Vector Machines 06K-Nearest Neighbors and Distances 07Unsupervised Learning 08Feature Engineering & Selection 09Model Evaluation 10Bias-Variance Tradeoff 11Ensemble Methods 12Hyperparameter Tuning 13ML Pipelines 14Naive Bayes 15Time Series Fundamentals 16Anomaly Detection 17Handling Imbalanced Data 18Feature Selection

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Phase 02

ML Fundamentals

01What Is Machine Learning 02Linear Regression 03Logistic Regression 04Decision Trees and Random Forests 05Support Vector Machines 06K-Nearest Neighbors and Distances 07Unsupervised Learning 08Feature Engineering & Selection 09Model Evaluation 10Bias-Variance Tradeoff 11Ensemble Methods 12Hyperparameter Tuning 13ML Pipelines 14Naive Bayes 15Time Series Fundamentals 16Anomaly Detection 17Handling Imbalanced Data 18Feature Selection

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Resources

Liu et al., Isolation Forest (2008)Reading Breunig et al., LOF: Identifying Density-Based Local Outliers (2000)Reading scikit-learn Outlier Detection docsReading Chandola et al., Anomaly Detection: A Survey (2009)Reading Goldstein and Uchida, A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms (2016)Reading Anomaly DetectorSkill