Diploma in Artificial Intelligence

Admission Requirements

• Typical Age: 18+
• Entry Requirements: Successful completion of a Level 4 qualification in Artificial Intelligence or related fields, or relevant work experience with demonstrated responsibilities in the field, is required.
• International Qualifications: Equivalent qualifications may include:
  • A high school diploma or recognised pre-university certification, plus at least one year of post-secondary study or additional course credits(e.g., Grade 12 followed by college-level course in Asia, Senior Certificate with further post-secondary education in Africa, Advanced Certificate or EQF Level 4 plus additional academic credits in Europe, or a high school diploma plus college-level or Advanced Placement(AP) in the US/Canada).
• English Proficiency (for non-native speakers): IELTS 5.5, CEFR level B2, or equivalent.
• Additional Guidance : For assistance with qualification equivalency or language requirements, please contact our admissions office.

Structure of the Qualification

• Total Credits: 120 ECTS
• Modules: 6 mandatory units, each worth 20 credits, providing advanced knowledge and practical skills in artificial intelligence.

Core Modules

1. Visualisation (20 Credits): Develops practical skills in data preparation, visual communication, and interpreting business insights using tools such as Tableau and Power BI.
2. Reinforced Machine Learning (20 Credits): Covers reinforcement learning principles, algorithms, libraries, and frameworks, along with model evaluation and tuning.
3. Natural Language Processing (20 Credits): Explores text preprocessing, key NLP libraries and methods, and sentiment analysis in business scenarios.
4. Human-AI Interaction (20 Credits): Examines user-centred design, human factors, multimodal interaction, explainable AI, and collaboration between people and AI systems.
5. Advanced Deep Learning (20 Credits): Builds on Level 4 with advanced neural network architectures, transfer learning, hyperparameter tuning, generative models, and applications in NLP and reinforcement learning.
6. Introduction to Computer Vision (20 Credits): Introduces image processing, feature extraction, object detection, image segmentation, and deep learning models for computer vision.

Explore the complete curriculum for this course to gain a detailed understanding of the topics covered, learning objectives, and key skills you’ll develop. See the full curriculum below.

View Curriculum

Assessment and  Regulations 

Assessment Approach

Assessment is based on a combination of advanced projects, use cases, and presentations, aligned with each unit’s learning outcomes. Assessments include:

• Written Assignments: Assessing comprehension and application of advanced AI principles, using examples from the workplace, case studies, or research.
• Presentations: Assessing learners’ ability to communicate AI concepts, findings, and business insights effectively.
• Projects and Case Studies: Practical tasks that relate to real-world AI issues, requiring critical thinking and problem-solving.

Grading System

Grades are awarded based on the successful completion of unit-specific assessment criteria:

• Distinction: 70% and above
• Merit: 60%–69%
• Pass: 40%–59%
• Fail: 0%–39%

Completion Requirements

Learners must pass all units by meeting all required learning outcomes and assessment criteria. All assessments are marked internally by the approved centre and externally moderated by Awarding Body to ensure quality and fairness.

Professional Development and Progression

Progress to Higher-Level Study:

• Progress to a Level 6 qualification in a relevant area.

Employment Opportunities:

This qualification prepares learners for roles such as:

• AI Developer
• Machine Learning Engineer
• NLP Engineer
• Computer Vision Engineer

Work-Based Learning:

• Practical tasks and case studies within each module support skill development and readiness for the workplace. Where possible, learners are encouraged to draw on workplace opportunities for research and investigation.