Introduction: End-to-End Machine Learning

Machine learning workflows are key to effective data science. This short course is focused on using Python packages to perform end-to-end data-driven analyses. 

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Course details

You will study four topics in this course. In three of those topics you will be engaging with worked examples of machine learning with a range of difficulty and scope. Advanced Python code is supplied and explained for the most advanced worked example. The remaining topic will introduce you to the machine learning landscape that reviews the terminology, concepts and performance metrics used in modern data science projects. The primary learning outcome for the course is the ability to manipulate data, fit models, summarise and display their results and performance, and objectively compare models prior to deployment.

Who is this course for?

The course is aimed at professionals with a good grasp of numeracy seeking to understand the core concepts and technologies that underpin modern machine learning. The topics detail three workflows, each of which uses collected data to derive models that will reliably and robustly predict new and unseen instances. 

The ability to contribute to such workflows is a core skill to boost your career in fields like: 

  • finance (fraud prevention and credit decisions)  

  • healthcare (diagnostic and prognostic decisions)  

  • marketing (targeted ads and customer retention) 

Teaching format

This is a self-paced online learning short course with lecture content, interactive elements, and access to a masterclass with the course leader after completion of the course.


Course requirements

Applicants do not need to be expert programmers but should be familiar with Python (notebooks, packages and basic data manipulation). The focus of the course is the use of released and curated Python machine learning code, rather than implementing algorithms from scratch. 

Coursework consists of the creation of code to solve a specific problem, together with a short report that describes the approach taken and critically evaluates the results. 

This code can be developed either using learners’ equipment (laptop, PC, etc.), or with cloud-based tools such as CoLab and Kaggle Notebooks, or Jupyter notebooks. A good internet connection is therefore more important than powerful computational equipment. 

The time commitment for this course is typically six to eight hours per week. 

Funding and scholarships

This course is eligible for funding from the Scottish Funding Council’s University Upskilling Fund. The University of St Andrews is able to offer a limited number of fully-funded Upskilling scholarships. 

To apply for a scholarship, please complete the Upskilling Eligibility Form


On successful completion of this online short course, you will be issued with a Certificate of Completion from the University of St Andrews.

Course dates

Start date:
Monday 15 January 2024
End date:
Monday 26 February 2024
42 days

Course teachers