Course Summary

The Advanced Diploma in Applied Artificial Intelligence is a practice-oriented programme designed to equip learners with the foundational and applied skills required to develop, deploy, and manage modern AI systems. The course adopts a hands-on, accessible approach, enabling learners with minimal prior experience to build real-world AI applications across multiple domains.

The programme covers six key areas: computer vision, conversational AI, deep learning, machine learning operations (MLOps), natural language processing (NLP), and generative AI systems. Learners begin by understanding how machines interpret visual and textual data, progressing to the development of intelligent systems such as chatbots, image classifiers, and simple AI-powered applications.

Throughout the course, emphasis is placed on practical implementation using existing libraries, frameworks, and APIs rather than complex mathematical theory. Learners will gain experience in designing workflows, integrating external data sources, deploying models, and applying prompt engineering techniques for generative AI systems. Ethical and responsible AI practices are embedded across all modules to ensure learners understand issues such as bias, privacy, and system reliability.

By the end of the programme, learners will be able to design and develop functional AI solutions, manage simple machine learning pipelines, and apply AI techniques to solve real-world problems in areas such as automation, customer service, and data-driven decision-making.

Course Structure

Full-Time Duration: 8 Months

Part-Time Duration: 8 Months

Delivery Mode: Blended

Instructional Method:  

• Lecture
• Tutorial
• Discussion
• Roleplay
• Presentations
• Storytelling

Assessment Method: Project Report

Assessment Weightage: Project Report (100%)

Teacher-Student Ratio: 1:24

Graduation Requirements: Students who complete and pass the modules stipulated in the course structure and fulfil attendance requirement will be issued with a “Advanced Diploma in Applied Artificial Intelligence (AI)” by United Ceres College

Admission Criteria

Qualification Entry Requirements:

• A Diploma from any institution or,
• GCE A Levels or equivalent

English Requirements:

• IELTS 6.0 or equivalent or,
• Pass in English in high school or,
• A score of 75 out of 100 in United Ceres College English Placement Test

Mature Applicants: 

• Candidates who are at least 30 years of age with at least 8 years of working experience may apply for admission as Mature Applicants. Candidates must submit resume and/ or other supporting documents as proof.

Minimum Age: 17

Intake: Every month

For specific intake dates and more details, please see Academic Calendar.

Course Fee

For detailed fee information, please refer to the Course Fee Page or contact us directly.

What You Will Learn

1. Principles of Computer Vision
This module covers core techniques such as image formation, filtering, edge detection, feature extraction, segmentation, and object recognition. Students will apply established libraries through hands-on labs and projects to build practical applications, gaining experience in developing foundational vision systems for domains such as robotics, security and healthcare

2. Conversational AI Systems Development
This module equips students with knowledge and practical skills in dialogue management, natural language understanding, and system integration. Through projects and labs, learners build multi-turn, API-integrated chatbots that deliver responsive, ethical, and context-aware interactions across domains such as healthcare, costumer service, and smart devices, gaining practical skills in real-world applications.

3. Deep Learning and Intelligent Vision
This module equips students with vision techniques using convolutional neural networks (CNNs), transfer learning, and modern architectures. Learners apply frameworks such as TensorFlow or PyTorch to design, train, and evaluate models, developing ethical solutions for domains such as autonomous driving, healthcare imaging and facial recognition, while gaining practical expertise for research and industry applications.

4. Machine Learning Operations
This module equips students with practical expertise in versioning, workflows, automated testing, continuous integrations/continuous deployment (CI/CD), and monitoring, preparing them for AI and machine learning operations roles to build scalable, reliable, and responsible AI pipelines. Students apply testing methods, explore workflow approaches, and complete projects that replicate real-world scenarios.

5. Natural Language Processing
This module introduces provides an overview of the concepts of Natural Language Processing (NLP) and its applications in virtual assistant (or chatbot), text classification, and Generative AI. Topics covered also include text pre-processing techniques, dialogue management and response generation, and large language models.

6. Generative AI Systems Development
This module immerses students in large language models, fine-tuning, and retrieval augmented generation, with hands-on focus on coding autonomous agentic systems, equipping graduates to build advanced and responsible AI solutions aligned with industry needs. Through projects, they explore agentic architectures, develop problem-solving strategies, and apply generative AI techniques to tackle complex, real-world challenges across industries.