AIDU-ML-102
Delivery Type: Live, instructor-led Remote or In person
Prerequisite: AI Foundations for Professionals
This course gives professionals a rigorous, non-technical understanding of how machine learning works in real organizational environments. Rather than focusing on tools or algorithms in isolation, it explains how ML systems behave across their full lifecycle, from problem framing and data pipelines to deployment, monitoring, and long-term maintenance.
Participants learn how major machine learning paradigms operate, what kinds of problems they can and cannot solve, and why headline accuracy and polished demos often fail to translate into real-world performance. Machine learning is treated as a socio-technical system shaped by data, incentives, human oversight, and organizational context.
The course emphasizes evaluation beyond accuracy, including drift, explainability, bias, safety, compliance, and economic tradeoffs. By the end, participants are equipped to assess ML systems critically, challenge vendor claims, and make informed decisions about where machine learning creates value and where it introduces unacceptable risk.
Core Topics:
What machine learning is and is not
Supervised learning algorithms and use cases
Unsupervised and semi-supervised learning
Reinforcement learning in real systems
Common ML application patterns across industries
Translating business problems into ML problems
Data pipelines, feature construction, and leakage
Model training versus real-world performance
Evaluation beyond accuracy and metric tradeoffs
Explainability and interpretability limits
Safety, ethical, risk, and compliance considerations
Limits and non-viable domains for ML deployment
Economic reality and ROI of machine learning
Outcomes:
Explain how ML systems generate predictions and differ from rules or automation
Distinguish between major ML paradigms and appropriate use cases
Evaluate ML performance beyond surface-level accuracy claims
Identify data, deployment, and lifecycle risks
Recognize compliance, safety, and governance obligations
Determine where machine learning delivers value and where it should not be deployed
