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"

  • Skills you'll gain: Model Deployment, Anomaly Detection, Model Training, Jupyter, Exploratory Data Analysis, Unsupervised Learning, Model Evaluation, Scientific Visualization, Data Visualization, Applied Machine Learning, Machine Learning Methods, Data Analysis, Data Preprocessing, Machine Learning, Development Environment

  • Skills you'll gain: Anomaly Detection, Microsoft Azure, Time Series Analysis and Forecasting, Generative AI, Query Languages, Data Integration, Process Optimization, Data Analysis, User Feedback

  • From the course: Azure Practical - Cognitive Services·Lesson: Anomaly Detection Services

  • From the course: Scenario and Root Cause Analysis with Generative AI·Lesson: Generative AI in root cause identification

  • From the course: Scenario and Root Cause Analysis with Generative AI·Lesson: Generative AI in root cause identification

  • From the course: Data Preparation and Evaluation with Copilot·Lesson: Data quality dimensions and anomalies

  • From the course: Data Processing with Azure·Lesson: 5.2 How Stream Analytics Support Native Windowing Functions to Enable Developers to Author Complex Stream Processing Jobs

  • From the course: Data Center Security Management with Microsoft System Center·Lesson: Behavioral Analytics and Anomaly Detection

  • From the course: Defending Against AI-Driven Phishing and Social Engineering·Lesson: Sample Size Analysis

  • From the course: Generative AI for Data Science·Lesson: Lesson 1: Versatility and Impact of GenAI in Data Science

  • From the course: Data Preparation and Evaluation with Copilot·Lesson: Data quality dimensions and anomalies

  • From the course: GenAI for Cybersecurity: Blue Team·Lesson: Network Analysis with AI