<ul data-eligibleForWebStory="true"><li>Machine learning is a practical engineering discipline, not magic or complex algorithms.</li><li>Success in ML lies in formulating the right business questions and choosing tools wisely.</li><li>From problem definition to real business results, understanding specific ML subfields is crucial.</li><li>Stages include question alignment, ML task formulation, data quality, model comparison, and deployment.</li><li>Key aspects involve business goal, success metrics, risks, constraints, and transformative ML applications.</li></ul>

A Practical Guide to Machine Learning for Business

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