Graduate Certificate in Applied Artificial Intelligence

Upon successful completion of the Graduate Certificate in Applied Artificial Intelligence, students who enrol in the Graduate Diploma or Master of Applied Artificial Intelligence degree will be granted credit for four subjects.

Upon successful completion of the Graduate Certificate in Applied AI, graduates will be able to:

1. Analyse real-world problems related to data, automation, and intelligence, and design practical AI solutions using modern machine learning, data engineering, and MLOps practices.    
2. Analyse the suitability of traditional and modern AI models and architectures for different data types and industry applications.            
3. Demonstrate professional and interpersonal competencies by communicating technical concepts effectively, collaborating, and applying ethical reasoning and leadership in applied AI initiatives.

Graduates will be prepared for roles such as:

1. AI Specialist / Consultant
2. AI Engineer
3. Machine Learning Engineer

The Graduate Certificate in Applied Artificial Intelligence is a 24-credit points (CPs) postgraduate degree designed to accommodate both technical and non-technical learners. The program structure supports a progressive, skill-based learning pathway, integrating foundational knowledge, and advanced AI practice.

The program consists of:

1. Foundation Component (4 subjects – 24 CPs): Designed for students from non-technical backgrounds, this stage covers essential skills in programming, databases, machine learning, and data engineering to ensure readiness for advanced topics.

Admission into the Graduate Certificate in Applied Artificial Intelligence requires:

• A recognised bachelor’s degree in a relevant field such as Computer Science, IT, Engineering, Data Science, or Business Information Systems; or any bachelor's degree combined with relevant professional experience and/or recognized industry certifications.
• A minimum English language proficiency of IELTS 6.0 overall (or equivalent) is required for admission.

Below are the subjects:

1. Foundations of AI & Machine Learning
   This subject provides an introduction to the foundational concepts, algorithms, and real-world applications of Artificial Intelligence (AI) and Machine Learning (ML). Students will develop practical skills in key ML techniques such as classification, clustering, association rule mining, neural networks, and reinforcement learning. The subject emphasizes hands-on learning, using modern tools and platforms to bridge theory with practice. Through applied assignments and workshops, students will learn to select, implement, and evaluate appropriate AI and ML techniques to solve practical problems across various domains. Ethical, legal, and trust-related considerations in AI applications will also be explored to promote responsible and informed AI practice.
2. Programming for AI Applications
   This subject introduces students to Python programming with a focus on data manipulation and machine learning applications. It is designed for students with little or no prior programming experience. Students will learn core Python concepts, how to work with key libraries such as NumPy, Pandas, and Scikit-learn, and how to build simple AI solutions. Through hands-on labs and coding workshops, students will develop skills in writing modular code, preprocessing data, and automating common AI pipeline tasks. By the end of the subject, students will have a foundational coding proficiency applicable to advanced AI subjects.
3. Databases & Cloud Fundamentals
   This subject provides foundational knowledge in databases and cloud computing, focusing on their essential roles in AI pipelines. Students will explore the principles of relational (SQL) and non-relational (NoSQL) databases, learning how to query, manage, and model data for AI applications. The subject also introduces key concepts in cloud computing, including cloud infrastructure, storage solutions, and deployment environments using platforms such as AWS and Google Cloud. Through practical exercises and guided projects, students will gain the technical skills needed to design data solutions and deploy scalable cloud-based environments that support modern AI workflows.
4. Data Engineering for AI
   This subject provides students with foundational knowledge and practical skills in data engineering, focusing on preparing quality data for AI and machine learning workflows. Students will learn how to collect, clean, transform, and manage data using modern tools and platforms such as Apache Airflow, Spark, and SQL-based ETL processes. Emphasis is placed on building scalable and reliable data pipelines, ensuring data integrity, and optimizing data flow for model training. The subject aims at equipping students with the technical fluency required to support AI applications in real-world environments.