Machine Learning with Python

Date: 27 February 2020
Time: 9.45am–5pm
Instructor: Peter Smyth
Level: Introductory
Fee: £120

CMI offers up to five subsidised places at a reduced rate of £60 per course day to research staff and students within Humanities at The University of Manchester. These places are awarded in order of application. 

Humanities PGR students at The University of Manchester can apply for a methods@manchester bursary to help cover their costs. All applications will be considered on a case-by-case basis and applicants will be required to provide a supporting statement from their supervisor.

Please click here to make a booking. If you are applying for a subsidised place, select the £60 University of Manchester option on the booking form. For queries about methods@manchester bursaries, contact (please note, you must have a confirmed place on the course before requesting a bursary application form). For any other queries about short courses, please contact

Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.


Part of Social Data Science Week, a collaboration between the Cathie Marsh Institute and methods@manchester. These courses have been designed for postgraduate researchers, giving you the tools to make use of free and open-source software, and techniques from the cutting-edge new discipline of social data science.

This intermediate course in Machine Learning Algorithms with Python will enable researchers to select the more appropriate machine learning algorithm to help answer their research questions. During the day we will look at a selection of ML algorithms, demonstrate how to use them in Python programs using appropriate Python packages like sklearn. Before using the algorithms, we will consider how well our data meets the assumptions made by the algorithms and we can manipulate the data so as to meet the assumptions.

Course objectives

On completion of this workshop, the participants will be able to:

  • Understand the different types of Machine Learning Algorithms
  • Understand any assumptions made
  • How to reformat data appropriately for the algorithm in use
  • Create models
  • Make predictions from the models
  • Review the accuracy of the models using available software functions  

After completion of this workshop, the participant will be in a position to clean and transform data to make it more useable for a variety of ML Algorithms. Know how to ensure that their data is in an appropriate format for applying a particular ML algorithm. Be aware of and understand any assumptions made by particular ML algorithms and understand how model accuracy is assessed and be familiar with the necessary software tools needed to perform the assessment.


Basic knowledge of the Python programming language.

No knowledge of Machine learning is assumed.

The workshop takes a very practical approach, so no mathematical knowledge is required.

About the instructor

Peter Smyth is a Research Associate at the University of Manchester, based in the Cathie Marsh Institute. He has spent 35 years working in IT at various large and small commercial organisations before taking an MSc in Big Data Analytics at Sheffield Hallam University and moving into academia. In his previous roles, he used any convenient programming environment to hand to solve problems. Now he teaches a variety of programming languages to help others to do the same.

He is a qualified Data and Software Carpentry instructor.