Machine Learning
Learn in-demand technical skills and their workplace applications in this applied machine learning short course.
Key facts
Fully funded places for this course are now closed. Self-funded applicants are still welcome to apply.
Overview
In the era of Big Data, machine learning and data analytics are vital to the success of any organisation. From simple sales forecasts to the AI behind self-driving cars, data are helping to drive continuous improvement. The techniques are powerful but need to be used with a full understanding of the subject. It is vital to understand best practice, and how an analytics project fits with the business objectives.
This is a technical short course, but it also has a very applied focus. You will learn the theory behind machine learning techniques such as regression, decision trees and neural networks, and you will learn how to apply them to different kinds of data. What’s more, you will learn how to conduct a machine learning project from start to end in a business setting.
The short course is taught by lecturers who have worked in both industry and academia in data science roles. They bring their experience of what it is like to work on commercial data analytics projects and prepare you to do the same.
Entrance requirements
This short course is not suitable for those who studied the University of Stirling's Data Analytics short course in 2021.
Self-funded applicants should have a minimum of a second-class honours degree or equivalent.
English language requirements
If English is not your first language you must have one of the following qualifications as evidence of your English language skills:
- IELTS Academic or UKVI 6.0 with a minimum of 5.5 in each sub-skill.
- Pearson Test of English (Academic) 56 overall with a minimum of 51 in each sub-skill.
- IBT TOEFL 78 overall with a minimum of 17 in listening, 18 in reading, 20 in speaking and 17 in writing.
See our information on English language requirements for more details on the language tests we accept and options to waive these requirements.
Objectives
You will learn how to apply machine learning to business and scientific applications. Both the practical aspects of the correct methodology and the theoretic underpinnings are covered so that you know what to do and why you are doing it.
At the end of the short course, you should be able to identify the business objectives that can be addressed using data analytics, apply the correct methodology to address them, and report the results to the rest of the business.
If you can program, you can conduct the exercises in Python. Otherwise, you can use a graphical user interface, which requires no programming at all.
Structure and content
The main topics on the short course are:
Data mining industry standards
- CRISP-DM and how to apply it
- Running a data-driven project and reporting results
The theory of statistical machine learning
- Train / Validate / Test best practice
- The bias-variance trade-off
- Cost minimisation and regularisation
Analytics techniques
- Linear and Logistic Regression
- Decision Trees
- Clustering Algorithms
- Neural networks
Delivery and assessment
The content is delivered online with recorded videos, exercises and written notes. The assessment involves a practical assignment designed to replicate the type of commercial data analytics project you could expect to carry out in a data analytics role.
Module coordinator
Employability
The skills taught in this short course are in high demand and salaries are also high. The short course is designed to teach you the skills and know-how you will need in an analytics role.
What next?
Contact us
If you have any questions about entry requirements for our continuing professional development and short courses, contact our Admissions team.
For all other questions, please use our enquiry form.