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Core machine learning concepts applied to power systems — supervised learning, regression, classification, and model evaluation using grid-relevant datasets. Builds …
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Core machine learning concepts applied to power systems — supervised learning, regression, classification, and model evaluation using grid-relevant datasets. Builds on EE301 Python foundations.
The second course in GIEE's CAIGE certification track. Students learn ML algorithms through real-world power engineering case studies — from load classification to anomaly detection in grid signals.
🚧 Coming Soon — sign up at giee.org for launch updates.
