Machine Learning I

Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods.

In this Machine Learning 1 module, learners will understand applications of the Support vector machine, K Nearest Neighbours and Naive Bayes algorithms for classification and regression problems. Additionally, students will develop practical machine learning and data science skills including theoretical basics of a broad range of machine learning concepts and methods with practical applications to sample datasets.

Reading List:

Introduction to Machine Learning with Python: A guide for Data Scientists, Andreas Müller and Sarah Guido, 1st Edition. (O’Reilly Media, 2016).

    Application requirements

    Candidates who apply for this course must have a recognised undergraduate degree or equivalent. Candidates without a degree but with other relevant qualifications and/or work experience can also be considered.


    English language competency at an IELTS 6.5 (or equivalent) is required of all applicants whose first language is not English. Where students can demonstrate previous substantial studies or work experience in English, this requirement can be waived.


  • Accreditation: ECTS Accredited (EQF7)
  • Total workload: 150 hours
  • Requires extra purchases (outside texts, etc.): Yes, purchases required
  • ID verification: Required
  • Admission requirements: Application required
  • Minimum education requirement for students: Undergraduate