Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries across the world, from healthcare and finance to cybersecurity, logistics and manufacturing. This course provides a practical introduction to the principles, techniques and applications of AI and Machine Learning, equipping learners with the knowledge and skills needed to understand, develop and evaluate intelligent systems.
During the first half of the course, learners will explore the foundations of AI, intelligent agents, AI applications, ethical considerations and the deployment of intelligent systems. In the second half, learners will focus on machine learning concepts, algorithms, data preparation and the development of machine learning solutions to solve real-world problems.
The course combines theoretical understanding with practical activities and provides an excellent foundation for further study or employment in the rapidly growing field of AI and data technologies.
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About the Course
The course combines two Pearson Higher National units:
- Unit 15: Fundamentals of Artificial Intelligence and Intelligent Systems (Weeks 1-8)
- Unit 25: Machine Learning (Weeks 9–16)
Teaching takes place over two evenings per week (days TBC), from 5:00pm to 8:00pm, providing a total of 6 hours of taught sessions each week.
What Will I Learn?
Unit 15: Fundamentals of Artificial Intelligence and Intelligent Systems (Weeks 1-8)
You will learn:
- The history, principles and foundations of Artificial Intelligence
- Differences between AI, Machine Learning and Intelligent Systems
- Types of AI including Narrow AI, General AI and Super Intelligence
- Intelligent agents and decision-making systems
- Real-world AI applications in business, healthcare, cybersecurity, logistics and engineering
- AI development approaches, tools and frameworks
- Machine learning concepts and common algorithms
- Data collection, preparation and validation techniques
- Computer vision, natural language processing and conversational AI
- Ethical, legal and security considerations in AI
- The opportunities and challenges associated with intelligent systems
Unit 25: Machine Learning (Weeks 9-16)
You will learn:
- Core concepts and terminology of Machine Learning
- Types of learning problems including classification, regression and clustering
- Supervised, unsupervised and reinforcement learning
- Popular machine learning algorithms such as:
- Decision Trees
- K-Nearest Neighbours (KNN)
- Support Vector Machines (SVM)
- Naive Bayes
- Linear Regression
- K-Means Clustering
- Data analysis and preparation techniques
- Training and testing machine learning models
- Developing machine learning applications using industry-standard tools and programming environments
- Evaluating model performance
- Understanding and addressing overfitting and underfitting
- Applying machine learning solutions to real-world business and technical problems
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Entry Requirements
This course is suitable for learners who have an interest in computing, data analysis, artificial intelligence or software development.
Recommended entry requirements:
- A Level 3 qualification in Computing, Digital Technologies or a related subject, or
- Relevant industry experience in IT, software development or data-related roles
- Basic computer literacy
- An understanding of fundamental computing concepts
- Basic programming knowledge is beneficial but not essential
Applicants without formal qualifications may be considered based on relevant experience and an interview or skills assessment.
Mature learners with relevant experience are encouraged to apply.
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Progression
Successful completion of this course can support progression to:
- Higher National Certificate (HNC) or Higher National Diploma (HND) in Computing
- Foundation Degree programmes
- Degree programmes in:
- Artificial Intelligence
- Data Science
- Computer Science
- Software Engineering
- Cyber Security
Employment opportunities including:
- Junior AI Developer
- Data Analyst
- Machine Learning Technician
- Business Intelligence Assistant
- AI Support Technician
- Software Developer
- Data Technician
Professional certifications (e.g., AI or cloud-based certifications): The knowledge gained will also support learners wishing to develop skills in emerging technologies, data analytics, intelligent systems and AI-enabled business solutions.
The course also builds transferable skills such as critical thinking, problem-solving, and data analysis which are valuable across the computing industry.
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Assessment Method
Assessment is completed through internally assessed coursework and practical assignments. There are no formal examinations.
Assessment activities may include:
- Written reports and evaluations
- Research-based assignments
- Case studies
- Presentations
- Practical laboratory activities
- Development and testing of AI and Machine Learning solutions
- Technical documentation
- Reflective evaluations of implemented systems
Learners will be required to demonstrate both theoretical understanding and practical application of AI and Machine Learning techniques to achieve the learning outcomes for each unit.
Assessment grading will follow Pass, Merit, and Distinction criteria for each unit.
