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ARTIFICIAL
INTELLIGENCE
Entry Requirements
Academic Requirements
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Learners are expected to hold a recognised secondary school qualification or equivalent.
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Mature applicants without formal qualifications may be considered on the basis of relevant work experience and their ability to meet the demands of the programme.
English Language Requirements
Learners must demonstrate English language proficiency sufficient to successfully engage with programme materials, participate in learning activities, and complete assessments. This may be evidenced through prior education completed in English or through a recognised English language qualification.
For learners whose first language is not English, an IELTS 5.5 (overall) or above is normally accepted for entry to the diploma.
Equivalent English language proficiency may include:
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PTE Academic: 42
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Duolingo English Test: 95
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TOEFL iBT: 46
Course Units
This unit introduces the fundamental concepts of artificial intelligence (AI), including its history, core principles, and real-world applications across industries. Learners explore how AI technologies such as machine learning, automation, and intelligent systems are transforming sectors including healthcare, finance, manufacturing, and education.
This unit introduces the fundamental concepts of artificial intelligence (AI), including its history, core principles, and real-world applications across industries. Learners explore how AI technologies such as machine learning, automation, and intelligent systems are transforming sectors including healthcare, finance, manufacturing, and education.
This unit introduces the fundamental concepts of artificial intelligence (AI), including its history, core principles, and real-world applications across industries. Learners explore how AI technologies such as machine learning, automation, and intelligent systems are transforming sectors including healthcare, finance, manufacturing, and education.
This unit examines the principles and technologies used to manage and analyze large-scale data. Learners explore big data architecture, data storage solutions, distributed computing frameworks, and the tools used to process and analyze large datasets in modern data-driven organizations.
This unit introduces the concepts and techniques behind deep learning and neural networks. Students explore how deep learning models are structured, trained, and applied to tasks such as image recognition, speech processing, and predictive analytics.
This unit explores the ethical, social, and legal considerations surrounding the development and use of artificial intelligence. Topics include algorithmic bias, transparency, accountability, privacy, and the responsible design and deployment of AI systems in society.
This unit focuses on techniques for transforming complex data into clear and meaningful visual representations. Learners explore principles of data visualization, visual analytics tools, and methods for communicating insights effectively through charts, dashboards, and interactive visual displays.
This unit introduces the principles of reinforcement learning, a machine learning approach where systems learn through interaction with their environment. Students explore reward-based learning models, decision-making algorithms, and practical applications such as robotics, game development, and autonomous systems.
This unit examines how computers process, understand, and generate human language. Learners explore key NLP techniques such as text classification, sentiment analysis, language modeling, and chatbot development using modern machine learning methods.
This unit builds on foundational deep learning knowledge and explores more advanced neural network architectures and techniques. Students examine convolutional neural networks, recurrent neural networks, model optimization, and real-world applications of deep learning in complex AI systems.
This unit introduces the principles and techniques used to enable computers to interpret and analyze visual information. Learners explore image processing, object detection, pattern recognition, and computer vision applications such as facial recognition, medical imaging, and autonomous vehicles.
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Artificial Intelligence
The Qualifi Level 5 Extended Diploma in Artificial Intelligence is a UK-regulated qualification designed to develop advanced knowledge and practical skills in the rapidly evolving field of AI. The programme explores key areas such as machine learning, data analysis, AI applications, and the ethical considerations surrounding emerging technologies.
Suitable for individuals seeking to build specialist expertise in artificial intelligence, the qualification also provides a fast-tracked pathway to the final year (Top-Up) of a related bachelor’s degree at many universities, offering a flexible and cost-effective route to completing a full undergraduate qualification.
Program Summary
Qualification Title
Level 5 - Extended Diploma in Artificial Intelligence
Course Credit
240
Course Duration
9 Months
Delivery Method
Online
Tuition Fee
£ 3145
Intake Dates
Monthly
University Progression
2-Years University Credit (UK, USA)
University Partner
Qualifi graduates can progress to the final year of a Bachelors Degree (Hons) at Arden University
