Dive deeper into understanding what drives your forecasts. You'll learn to interpret forecast components, identify seasonal patterns, and recognize trends that impact prediction accuracy.

Learn to measure and improve forecast accuracy using statistical metrics. You'll calculate MAPE, understand prediction intervals, and iteratively refine parameters to achieve better predictions.

Transform your forecasts into professional deliverables. You'll learn to export forecast data, create assumption documentation, and build forecast packs that support financial planning processes.

Launch your first machine learning forecast using Azure AutoML's no-code interface. You'll configure experiments, select algorithms, and generate predictions that capture complex patterns traditional methods miss.

Unlock the "why" behind ML predictions through feature importance analysis. You'll interpret which variables most influence forecasts and translate technical outputs into business insights.

Rigorously assess your ML model's performance against benchmarks. You'll calculate key metrics, compare to naive forecasts, and determine if ML adds sufficient value to justify complexity.

Create new ML model versions through systematic experimentation and parameter optimization. You'll document each iteration and create deployment-ready documentation for ongoing use.

Apply your forecasting skills by creating both statistical and ML-based revenue forecasts, comparing their accuracy, and recommending the optimal approach for your organization's FP&A process.