C4. Build a Model for Anomaly Detection in Time Series Data (Pratheerth Padman, 2022)

1. Course Overview: 1. Course Overview 00:00:00 2. Introduction to Time Series Data and Anomaly Detection: 1. Course and Module Summary 00:01:50 2. What Is Time Series Data 00:04:15 3. Analysing Time Series Data 00:09:05 4. Stationarity and Autocorrelation 00:12:54 5. Introduction to Anomaly Detection 00:17:45 6. Demo - Setting up Your Environment 00:21:29 7. Module Summary 00:24:33 3. Building a Model to Automate Anomaly Detection: 01. Module Overview 00:25:40 02. STL Decomposition 00:26:41 03. Classification and Regression Trees (CART) 00:28:42 04. Clustering-based Anomaly Detection 00:32:42 05. Anomaly Detection Using Autoencoders 00:36:05 06. Demo - Introduction to the Problem and Dataset 00:38:48 07. Demo - Exploratory Data Analysis and Data Cleaning 00:41:17 08. Demo - Data Preprocessing and Dimensionality Reduction 00:49:34 09. Demo - Building a Model for Anomaly Detection 00:55:33 10. Module Summary 01:01:39 4. Model Evaluation and Dealing with Anomalies: 1. Module Overview 01:02:57 2. Demo - Evaluating the Anomaly Detection Models 01:03:40 3. How to Deal with Anomalies 01:10:35 4. Module Summary and Feedback 01:12:43