Deepdive Into Machine Learning Using Autonomous Database

Course Description

Deepdive Into Machine Learning Using Autonomous Database develops practical capability in in-database machine learning on Oracle Autonomous Database from data preparation through governed scoring. The course is organized around a customer-retention use case where sensitive warehouse data must be prepared, modeled, explained, and scored without unnecessary movement.

Learners move through four connected stages: Autonomous Database Data Preparation for Machine Learning; Oracle Machine Learning Algorithms and Model Building; Evaluation, Explainability, and Responsible Use; and Deployment, Batch Scoring, Monitoring, and Governance. Each stage links technical or business decisions to measurable evidence, risk controls, and operational ownership.

The content is independently written for SkillDynamo using the supplied source page for the verified title, update date, duration, and topic alignment. Learners finish with tested outputs, documented exceptions, troubleshooting evidence, and a prioritized improvement action.

What You Will Learn

• Explain and apply schema access, SQL-based exploration, distributions, missing values, and leakage within Deepdive Into Machine Learning Using Autonomous Database.
• Configure, analyze, validate, or troubleshoot feature selection, transformation, binning, normalization, and categorical handling, training, validation, and holdout strategy inside the database, and data lineage, reproducible preparation scripts, and quality acceptance criteria using course-specific evidence.
• Explain and apply classification, regression, clustering, anomaly detection, and association choices within Deepdive Into Machine Learning Using Autonomous Database.
• Configure, analyze, validate, or troubleshoot OML SQL and notebook workflows for model creation, algorithm settings, sampling, class imbalance, and model metadata, and experiment naming, parameter capture, repeatability, and model inventory using course-specific evidence.
• Explain and apply confusion matrix, ROC, precision, recall, lift, and business threshold selection within Deepdive Into Machine Learning Using Autonomous Database.
• Configure, analyze, validate, or troubleshoot residual analysis and evaluation measures for numeric prediction, feature importance, prediction details, bias checks, and explanation limits, and privacy, access control, acceptable-use review, and model approval evidence using course-specific evidence.
• Explain and apply SQL prediction functions, views, batch scoring, and application consumption within Deepdive Into Machine Learning Using Autonomous Database.
• Configure, analyze, validate, or troubleshoot model permissions, versioning, promotion, and rollback, data drift, performance drift, operational monitoring, and retraining triggers, and scoring validation, runbook, ownership, incident response, and improvement plan using course-specific evidence.

SkillDynamo Training Approach

The training follows a customer-retention use case where sensitive warehouse data must be prepared, modeled, explained, and scored without unnecessary movement. Activities combine course-specific demonstrations, guided implementation, hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, solution review, and a final improvement plan. Learners document assumptions, settings, commands or transactions, outputs, exceptions, corrective actions, governance trade-offs, rollback needs, and operational ownership.