Anomaly Detection & Outlier Analytics: Mastering Isolation Forest, One-Class SVM, LOF, and Time Series for Fraud.
Sub Category
- Other IT & Software
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Objectives
- 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.
Pre Requisites
- Solid understanding of Python programming (intermediate level is required).
- Familiarity with foundational statistics and probability concepts.
- Experience using common Python data science libraries like NumPy and Pandas.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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