This eCourse consists of two modules on supervised machine learning (S-ML). Artificial intelligence systems can help solve business problems by answering questions and facilitating data-informed decisions. Machine learning (ML) programs – which fall into two categories, supervised and unsupervised – use raw data to develop models that can produce actionable business intelligence. Both supervised and unsupervised ML systems can thus support business processes and inform both day-to-day and strategic decisions.

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.

Module 1 provides an overview of S-ML, including its advantages, limitations, and appropriate use cases.

Module 2 explores the use of S-ML systems in practice and the data and mathematics that underpin their outputs.

On completion of this course, you will be able to:
- Distinguish between supervised and unsupervised machine learning
- Define supervised machine learning and identify its limitations and use cases
- 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

Module 1 - Supervised Machine Learning – An Introduction
Topic 1: Machine Learning Overview
Topic 2: Supervised Machine Learning (S-ML)

Module 2 - Supervised Machine Learning In Practice
Topic 1: Overview of Practical S-ML
Topic 2: Data & Methodology
Topic 3: Training an S-ML System