• Collect clean and engineer features from security telemetry and operational data

• Apply supervised and unsupervised learning techniques for anomaly and threat detection

• Build models that reduce alert fatigue and improve detection precision

• Evaluate model performance including bias testing validation and explainability

• Deploy machine learning pipelines using Python and industry standard frameworks

• Integrate machine learning capabilities into SIEM and SOAR workflows

Who This Course Is For
This course is designed for cybersecurity professionals SOC analysts security engineers threat hunters incident responders and technical practitioners seeking to apply data science and machine learning to security operations.

Hands On Training Experience
Learners complete extensive hands on labs using realistic security data sets. Exercises include developing anomaly detection systems malware classification models and automated pipelines that support operational security use cases.