1. Demonstrate systematic understanding through practical use, of advanced Machine Learning and Natural Language Processing techniques and concepts through the practical application to applied to a range of design scenarios. (AHEP 4: M2, M3, M4, M5)

Knowledge and Understanding, Analysis, Application

2. Develop, refine, and critically evaluate Machine Learning models using Python and industry standard tools (with security consideration). (AHEP 4: M2, M3, M5)

Enquiry, Problem Solving, Application

3. Devise complex solutions to real-world problems and innovative AI artefacts using advanced Machine Learning models. (AHEP 4: M2, M3, M5)

Problem Solving, Application

4. Leverage the power of data through analysis and modelling and evaluate advanced Machine Learning methods to improve predictive performance and refinement to decision-making strategies. (AHEP 4: M1, M2, M4, M5, M17)

Enquiry, Reflection

Written Report – An individual written report coursework weighted at 50% that evaluates the students' comprehension critically on the understanding of Natural Language Processing and Machine Learning and their application to problem design scenarios. The report will need to discuss how the student has practically approached problems and found solutions through using the appropriate techniques and concepts. The report will focus on practical application, with the final aspect of it discussing predictive performance in data decision-making strategies (Learning Outcomes 1, 3 and 4). Meeting AHEP 4 Outcomes: M1, M2, M3, M4, M5, M17.

Practical Assessment - An implementation and testing of an artefact weighting at 50%, which uses advanced features of the Python programming language. Students will show and describe their solution in a demonstration providing rationale as to its development and the AI techniques modelled (e.g. including NLP and ML approaches) (Learning Outcome 2 and 3). Meeting AHEP 4 Outcomes: M2, M3, M5.

Professional Body requirements mean that a minimum overall score of 50% is required to pass a module, with each element of assessment requiring a minimum mark of 40%.

This module will cover the following topics:

Applications – by looking at developing an appreciation of AI and its specific usage within applications. There will be a focus on application of design representation and modelling notations, costs and savings through using AI based approaches, evaluation and trade-off of created artefacts, commercial aspects of AI App development, legal and commercial aspects of AI, and security considerations in applications

Machine Learning and Natural Language Processing – through core Natural language processing (NLP) and Machine Learning themes including: Text mining, Business requirements design, The science of extracting insights from large amounts of natural language, NLP and its relationship to artificial intelligence, Data modelling for sharing information, NLP methods and applications including language models, machine translation, and parsing algorithms for syntax and the deeper meaning of text, Deep learning approaches specifically designed for NLP tasks, and Case studies and practical exercises

Python Programming – by examining industry standard practice, principles and theory, Program design and alternative solution generation, Algorithm design and associated maths-based elements in relation to programming, Security issues and consideration in finding solutions, Advanced features of programming, Generic classes and collections, Inner and anonymous classes, Lambda expressions, Enumerated types, Cloning, Auto-closable resources, Custom exceptions, Mutable vs Immutable objects, and Functional interfaces.

This module explores the areas of Natural language processing (NLP) and Machine Learning, key emerging areas in computing. Students will explore these areas in depth looking at aspects of text mining, and how to make the best use of the technologies. There will be a focus on data modelling and design. Allied to this the module will take designs and implement these practically using the Python programming language. In undertaking the programming students will learn some of the advanced features available. This will add a degree of sophistication to coding skills they will already possess.

Theory will be delivered via lectures and supported by practical classes, seminars and discussion groups. In addition, you will be provided with a range of resources for independent study such as case studies, academic papers and industry stories. There will be a mixture of practical and theoretical formative exercises which will help you build your knowledge and confidence as well as preparing you for the summative assessment.

Fridman, L. (2025), AI & ML with Python: Deep Learning for Artificial Intelligence and Machine Learning Third Edition 2025: The Right way to Learn AI python for Beginners, Independently published

Mitchell, R. D., (2025), AI And Machine Learning for Beginners 2025: Essential Guide to Building Intelligent Systems with Python, AI And ML Including Hands-On Projects and Real-world Examples, Kindle Edition

Gates, S. (2025), Python Programming for Beginners: A Complete Step-by-Step Guide to Mastering Python Coding in Less Than a Month, Independently Published

Jackline, S. (2022), Python for Beginners 2022: Super Course Python Programming: Training Course: Learning to Code Very Easy, Kindle Edition

Software for referencing (eg Mendeley, Zotero, Refworks), mind mapping
Python for programming aspects