Anomaly Detection

Anomaly Detection is a data analysis process that identifies data points, events, or observations that deviate from the established norm or behavior. Coursera's Anomaly Detection catalogue schools you on the advanced techniques used to detect abnormal patterns or anomalies in data. You'll learn about different types of anomalies and the statistical techniques used to distinguish them, how to build predictive models for anomaly detection, and how to apply these models in various fields such as cybersecurity, finance, health, and many more. This skill will also equip you with the ability to use machine learning and AI for detecting anomalies, enhancing your capabilities as a data scientist, software engineer or any professional dealing with big data.

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Results for "Anomaly Detection"

  • University of Colorado Boulder

    From the course: Introduction to Machine Learning: Unsupervised Learning·Lesson: Other Applications of Unsupervised Learning

  • Skills you'll gain: Model Based Systems Engineering, Peer Review, Programmable Logic Controllers, Systems Engineering, Product Lifecycle Management, Intrusion Detection and Prevention, Security Controls, Performance Measurement, Collaborative Software, Control Systems, Continuous Monitoring, Anomaly Detection, Manufacturing and Production, Machine Controls, Incident Response, Digital Transformation, Cyber Attacks, Vulnerability Assessments, Cybersecurity, Manufacturing Operations

  • Macquarie University

    Skills you'll gain: Responsible AI, Incident Response, AI Security, Computer Security Incident Management, Incident Management, Fraud detection, Threat Detection, Security Management, Anomaly Detection, Security Testing, Cyber Attacks, Threat Modeling, Data Ethics, Cybersecurity, Cyber Operations, Network Security, Malware Protection, Crisis Management, Machine Learning, Generative Adversarial Networks (GANs)

  • From the course: Advanced Manufacturing Process Analysis·Lesson: 2. Anomaly Detection

  • University of Colorado Boulder

    Skills you'll gain: Unsupervised Learning, Anomaly Detection, Data Preprocessing, Data Mining, Feature Engineering, Data Analysis, Algorithms

  • From the course: Exploratory Analytics Project Ideation·Lesson: Module 1 Part 3: Exploratory Methods II: Clustering Analysis & Anomaly Detection

  • From the course: Unsupervised Learning and Its Applications in Marketing·Lesson: Introduction to Anomaly Detection

  • Skills you'll gain: Unsupervised Learning, Data Mining, Social Network Analysis, ChatGPT, Embeddings, LLM Application, Applied Machine Learning, Data Quality, Unstructured Data, Anomaly Detection, Machine Learning Methods, Data Science, Machine Learning, Data Preprocessing, Data Transformation, Data Analysis, Social Media Analytics, Data Manipulation, Python Programming, Exploratory Data Analysis

  • University of Colorado Boulder

    From the course: Data Mining Methods·Lesson: Types of Outliers, Outlier Detection Methods

  • University of Colorado Boulder

    From the course: Data Mining Project·Lesson: Project Proposal

  • From the course: Unsupervised Learning and Its Applications in Marketing·Lesson: Nonlinear Anomaly Detection

  • O.P. Jindal Global University

    From the course: Unsupervised Learning and Its Applications in Marketing·Lesson: Normal PCA Anomaly Detection