Supervised Machine Learning (S-ML) systems are widely used across business sectors. While they can help facilitate faster and better decision-making, however, there are pitfalls associated with their use. It is, therefore, important to understand their limitations before deploying such systems. It is also necessary to understand what types of data are needed to build effective S-ML systems and how such systems use data to achieve their outputs.
This course explores the use of S-ML systems in practice and the data and mathematics that underpin their outputs.
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