AnyML Anomaly Detection Model - Automated Machine Learning Platform скачать в хорошем качестве

AnyML Anomaly Detection Model - Automated Machine Learning Platform

🔍 What you'll learn in this video: How to set up an anomaly detection experiment in AnyML—uploading your dataset and selecting the proper settings for unsupervised detection. The fundamentals of anomaly/outlier detection, including how AI and machine learning automate identifying unusual data points. Real-world use cases where anomaly detection shines—fraud detection, manufacturing defects, cybersecurity, and healthcare monitoring. How to configure advanced settings, such as detection thresholds and multivariate feature selection, and when it’s best to use default options. Navigating the leaderboard to assess and compare various anomaly detection models ranked by the AnyML Score. In-depth exploration of the Model Report: view anomaly rankings, scatter plots, and score histograms (from normal to highly unusual). Exporting anomaly results securely through CSV reports, with options to include or exclude detected anomalies. Understanding that different algorithms may flag different anomalies—and using the AnyML Score to select the best model for your data. How to use Scenario Builder in anomaly detection—score new entries manually with Prediction Mode or process entire CSV files in Batch Mode. By the end of this video, you'll confidently walk through creating, evaluating, and exporting anomaly detection models using AnyML—equipping you to uncover critical insights and outliers in your own data. For more resources or support, visit anyml.com or contact info@anyml.com.