INFORMATION TECHNOLOGY PROGRAMS
Level 4 Diploma in Artificial Intelligence
Start your journey into intelligent systems and smart technologies.
Course Overview
Level 4 Diploma in Artificial Intelligence is designed to provide learners with a strong foundation in AI principles and their practical applications. The course covers a wide range of essential topics including the history, theories, and principles of AI, machine learning, data science, deep learning, and AI ethics. Students will also gain hands-on experience with popular AI tools and develop programming skills, particularly in Python.
The aim of this qualification is to:
Equip learners with the fundamental knowledge required to understand and apply AI concepts and techniques.
Introduce machine learning algorithms and provide practical skills in AI programming to solve real-world problems.
Provide a deep understanding of data science concepts, including data cleaning, transformation, and feature engineering, necessary for AI applications.
Develop a critical awareness of the ethical issues and challenges related to the use of AI technologies in various sectors.
Offer students the opportunity to work on real-world projects, using their knowledge to develop practical AI solutions.
Prepare students for entry-level roles in AI development, machine learning engineering, and data science, or provide a pathway to further academic study in AI or related fields.
Entry Requirements
The QUALIFI Level 4 Diploma in Artificial Intelligence is accessible to learners from a range of academic and professional backgrounds. Entry is based on a centre interview, and applicants should meet the following criteria:
Academic Requirements: A Level 3 qualification (or equivalent) in a relevant field such as computer science, mathematics, or engineering.
Professional Experience: Alternatively, applicants with relevant work experience in sectors such as technology, government, or the non-governmental sector may also be considered, provided they demonstrate ambition and a clear career direction in AI.
Applicants may be asked to provide references or statements outlining their experience and interest in the field of Artificial Intelligence.
Qualification Structure
The Level 4 Diploma in Artificial Intelligence consists of six mandatory units, for a total of 120 credits. All units must be completed for successful qualification.
Mandatory Units:
Introduction to Artificial Intelligence and Applications – 20 credits
- AI principles and foundation, its technologies and impacts on society
- Trending AI application and their benefits in industries such as Education, Marketing
and Small Businesses
- AI models and its purposes (Neural network, supervised, unsupervised, reinforced
learning)
- AI technologies including data science, machine learning, natural language processing,
computer vision, speech/image recognition and robotics
- Implementation of AI and its challenges
Mathematical Foundations for Machine Learning – 20 credits
- Probability, Statistics, and Data Analysis
- Inferential Statistics and Hypothesis Testing
- Regression Analysis and Calculus
- Some Advanced Calculus and Computational Complexity
Data Science Using Python – 20 credits
- Python Basics and Introduction to Data Science
- Coding and use of key libraries like Pandas and NumPy.
- Data Manipulation and Visualization
- Matplotlib and Seaborn
- Exploratory Data Analysis and Basic Statistics
- Machine Learning in Python
- Mean, median, mode, variance
Big Data Management – 20 credits
- Fundamentals of Big Data and Technologies
- Hadoop and NoSQL, MapReduce, Spark databases
- Data Mining and Processing
- Classification or clustering
- Data Visualization and its applications in industries such as retail eCommerce, Public
Relation, and Human Resources
- Tableau or PowerBI
- Ethical Implications in optimizing big data
Introduction to Deep Learning – 20 credits
- Fundamental concepts in deep learning and Neural network architecture
- Classification, Training / Testing Data, Overfitting and Underfitting.
- Neural network models and their derivable outcome
- Neural networks, backpropagation, activation functions and optimizers s
- Data preparation and preprocessing
- Model evaluation and tuning.
Artificial Intelligence Ethics – 20 credits
- Ethical implications of AI technologies and AI ethical framework
- Privacy, fairness, transparency, and accountability.
- Challenges of bias AI algorithms and its implication on fairness
- Methods to examine and mitigate ethical issues in AI systems
- Existing regulatory landscape in governing AI systems
Total Qualification Time (TQT): 1200 hours
Guided Learning Hours (GLH): 720 hours
Total Credits: 120
Each unit provides learners with essential knowledge and skills that are directly applicable to the rapidly evolving field of Artificial Intelligence.
Key Outcomes
The learning outcomes for the Level 4 Diploma in Artificial Intelligence are as follows:
Understanding AI Concepts: Demonstrate a solid understanding of artificial intelligence theories, concepts, and principles.
Machine Learning Application: Apply machine learning techniques to real-world challenges and solve complex problems through data analysis.
Data Science Proficiency: Analyze, clean, transform, and process data for AI applications, ensuring data quality and suitability for machine learning.
AI Solution Development: Develop basic AI models and solutions using programming languages like Python, with an emphasis on practical applications.
Ethical Awareness: Critically evaluate the ethical implications and legal considerations of AI technologies, understanding their societal and industry impacts.
Societal Impact: Identify and analyze the broader societal, economic, and industry-related effects of AI, contributing to responsible and informed AI development.
These learning outcomes are designed to ensure that learners are equipped with both the theoretical knowledge and practical skills needed for a career in AI or further study in the field.
Duration and Delivery
This qualification is designed to be flexible and accessible to a diverse range of learners. It can typically be completed over one academic year, depending on the mode of study (full-time or part-time) and the learner's pace.
Delivery methods may include:
- Instructor-led classroom sessions
- Online learning modules
- Self-directed study
- Group projects and collaborative tasks
- Practical IT labs and case studies
Assessment and Verification
The course involves a combination of exams, assignments, and project work to assess students' understanding of AI concepts and their ability to apply them practically.
Upon successful completion, learners will be well-prepared to pursue careers in AI, data science, or related fields, or continue their education in advanced AI studies.
Career & Progression Opportunities
This qualification ensures that learners gain the foundational knowledge and practical expertise required to embark on a career in AI, data science, or machine learning. With a strong emphasis on ethical considerations and real-world applications, graduates are equipped for success in a rapidly evolving field.