Choose Cost-Effective ML Algorithms Fast teaches you how to evaluate and compare machine learning algorithms based on their resource utilization—not just accuracy. In real ML pipelines, training time, memory footprint, and compute cost determine whether a model can run reliably at scale. In this short, practical course, you’ll examine how algorithm design affects efficiency, learn how to benchmark models fairly, and interpret logs to uncover cost patterns. You’ll complete a hands-on lab comparing XGBoost and Random Forest on a large dataset, charting training time and memory usage, and making a clear recommendation for the most cost-effective option. By the end of the course, you’ll know how to select algorithms that meet performance goals while staying efficient, predictable, and production-ready.