Supervised Machine Learning In Practice

Objectives On completion of this tutorial, you will be able to: List the key factors to consider when using a supervised machine learning system to solve a problem Identify the types of data required to train a supervised machine learning system Recall the process used to train a supervised machine learning system, including the mathematical techniques used Tutorial Overview 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 tutorial explores the use of S-ML systems in practice and the data and mathematics that underpin their outputs. Prerequisite Knowledge Supervised Machine Learning - An Introduction Tutorial Level: Intermediate Tutorial Duration: 50 minutes