People in business or industry who wish to develop technical skills in machine learning.
![A programmer writing code for a machine learning algorithm in Python]](/microcredentials/t4media/machine-learning-python-wide.gif)
Machine Learning with Python
Overview
| Module Code | COMP47990 |
| Module Title | Machine Learning with Python |
| Subject Area | Computer Science |
| Credits | 5 |
| NFQ | 9 |
| EFQ | 7 |
| Start Date | Spring |
| Duration | 12 Weeks |
| Time | Wednesdays 6pm – 7pm (Subject to change.) |
| Mode of Delivery | Online |
| Course Leader | Professor Pádraig Cunningham |
| Fee | €900 |
| Application Deadline | December 2025 |
The objective of this module is to familiarise students with the fundamental theoretical concepts in machine learning, as well as to instruct students in the practical aspects of applying machine learning algorithms. Key techniques in supervised machine learning will be covered, such as classification using decision trees and nearest neighbour algorithms, and regression analysis. A particular emphasis will be placed on the evaluation of the performance of these algorithms. In unsupervised machine learning, a number of popular clustering algorithms will be presented in detail. Further topics covered include ensemble learning, dimension reduction and model selection. This module requires strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts. Exercises and assignments will use the machine learning libraries in Python.
On completion of this module, students will be able to:
1) Distinguish between the different categories of machine learning algorithms;
2) Identify a suitable machine learning algorithm for a given application or task;
3) Run and evaluate the performance of a range of algorithms on real datasets using Python libraries.
Supervised Learning
k-Nearest Neighbour
Regression
Logistic Regression
Neural Networks
Decision Trees
Naive Bayes
Evaluation
Model Selection
Ensembles
Dimension Reduction
PCA
Feature Selection
Clustering
Standard Models
Spectral Clustering
On completion of this module students will have a good understanding of how machine learning works. Generative AI models such as Chat GPT are machine learning models.
Lecture materials are available online as pre-recorded lectures. There will be one hour synchronous online labs/tutorials each week.
Good mathematical ability. Prior programming experience in a high-level language (but not necessarily in Python).
- Practical Assignment (20% of the grade)
- Practical Assignment (20% of the grade)
- 1 hour written exam (60% of grade)
- Feedback individually to students, post-assessment
- Group/class feedback, post-assessment
Data Science in Python
Please note: Learners can avail of only one form of funding per application.
Micro-Credentials Learner Fee Subsidy-Human Capital Initiative Pillar 3
The HCI Pillar 3 Micro-credential Learner Fee Subsidy has been introduced to enable more learners to address critical skills gaps and engage with lifelong learning through micro-credentials. The HCI Pillar 3 Micro-credential Learner Fee Subsidy is funded by Higher Education Authority (HEA) and the Department of Further and Higher Education, Research, Innovation and Science.
HCI Micro-credential Learner Fee Subsidies are available on identified micro-credentials only and in fixed numbers from March 2024 until October 2025.
Please see Eligibility Criteria for further information.
On successful completion of this micro-credential, you will receive credits as per the European Credit and Transfer System. These credits are recognised by the awarding institution as credits aligned to learning completed at postgraduate level.
If you have any questions about this micro-credential, or would like to speak to a UCD staff member, please contact (opens in a new window)microcredentials@ucd.ie.