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Certified Anomaly Detection & Outlier Analytics
Rating: 4.2 out of 5(2 ratings)
2,090 students

Certified Anomaly Detection & Outlier Analytics

Anomaly Detection & Outlier Analytics: Mastering Isolation Forest, One-Class SVM, LOF, and Time Series for Fraud.
Created byMuhammad Shafiq
Last updated 11/2025
English

What you'll learn

  • Master the theoretical concepts behind defining and classifying outliers and anomalies (point, contextual, and collective).
  • Implement foundational statistical methods like Z-Score, IQR, and Box-Plot visualization in Python and Pandas.
  • Execute unsupervised detection algorithms including Isolation Forest (iForest) and Local Outlier Factor (LOF).
  • Apply kernel-based and density-based methods, specifically One-Class Support Vector Machines (OC-SVM).
  • Develop robust preprocessing pipelines tailored for handling extreme class imbalance issues common in anomaly datasets.
  • Design and evaluate anomaly detection models using specialized metrics like Precision-Recall curves and F1 scores.

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Included in This Course

15 questions

  • Quiz 35 questions
  • Quiz 25 questions
  • Quiz 15 questions

Description

  • Certified Anomaly Detection Expert: Project-Based Training


  • This isn't just a course; it's a project-based certification designed to make you an expert in Anomaly Detection & Outlier Analytics. Mastering anomaly detection is crucial for stopping fraud, securing systems against intrusions, and enabling precise predictive maintenance.

  • We move far beyond basic statistics straight into state-of-the-art machine learning. The core of this program is practical application using Python, Scikit-learn, and specialized libraries like PyOD. You’ll tackle real-world case studies, including credit card fraud and industrial equipment failure prediction using actual datasets. This hands-on approach ensures you gain skills immediately applicable in the industry.

  • The curriculum systematically covers supervised, unsupervised, and semi-supervised techniques. You'll dive deep into essential algorithms like Isolation Forest (iForest), Local Outlier Factor (LOF), and One-Class SVM (OC-SVM). We also cover advanced methods for time series data, including deep learning approaches. We emphasize proper data preparation and feature engineering, which are vital for model success.

  • Upon completion, you won't just know the concepts; you'll be ready for production-level deployment. You'll be proficient in model building, result interpretation, and expertly handling the tough challenge of class imbalance inherent in outlier problems. This expertise will make you a highly sought-after specialist in any data science team. Get certified and transform your career.

Who this course is for:

  • Data Scientists looking to specialize in fraud detection, cybersecurity, or industrial predictive maintenance.
  • Machine Learning Engineers responsible for monitoring system health and identifying system failures.
  • Risk Management Professionals requiring advanced statistical and ML tools for outlier analysis.

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