Introduction

This course covers an in-depth exploration of leveraging machine learning techniques to enhance decision-making and efficiency in public sector organisations. Participants will learn how to apply advanced data analytics to tackle real-world challenges such as resource allocation, and policy development amongst others. Topics such as key machine learning algorithms, data preprocessing and model evaluation are covered in this course which tailors specifically to the unique needs and constraints of public sector applications. By engaging in practical case studies, participants will gain the knowledge and exposure to transform raw data into actionable insights, ultimately driving better outcomes and promoting transparency in public services.

Learning Outcome

At the end of the program, participants will be able to:

• Understand Machine Learning Techniques i.e. machine learning algorithms, such as regression, classification, and clustering, to analyse public sector datasets and extract meaningful insights for improved decision-making.
• Understand the data pre-processing skills such as data cleaning, transformation, and normalisation techniques to prepare complex public sector data for effective analysis and modelling.
• Evaluate the performance of machine learning models using appropriate metrics and techniques, and apply strategies to optimise models for accuracy and efficiency in public sector applications.
• Utilise machine learning to address specific challenges in the public sector, such as resource allocation, fraud detection, service delivery improvement etc. through case studies.
• Analyse and apply ethical considerations and best practices related to data privacy, transparency, and fairness when deploying machine learning solutions in public sector contexts.

Duration

3 Days (19.5 Hours)

Target Participant

• Division I (ES1)
• Division II (ES2)
• Division III (ES3)

Language

English