Machine Learning using Python
When: 3 - 20 February 2026 or 16 June - 3 July 2026
Where: TBA
Machine Learning using Python
Machine Learning is the Art and Science of learning patterns from data. It includes the most recent LLMs, such as ChatGPT, which have learned the patterns of human language from all the text available online to such an extent as to be a really helpful conversation partner, but also the less visible algorithms that recommend the next show you should watch on Netflix, or which detect when some unusual activity has been happening on your credit card.
Content
In this course, you will get an overview of Machine Learning, starting with a basic introduction to its underlying concepts.
✅ You will explore the different types of algorithms, their uses, and limitations, and you will also touch on the most advanced, but computationally demanding vision and language algorithms.
✅ You will learn how to preprocess data, select models, and evaluate performance, with a focus on hands-on examples.
✅ You will work with algorithms for regression, classification, and clustering, as well as delve into neural networks and deep learning.
✅ Throughout the course, you will build practical skills in Python using libraries like scikit-learn, pandas, and TensorFlow.
✅ You will also tackle challenges such as overfitting, feature selection, and hyperparameter tuning.
Result
By the end of the course, you will be ready to start applying Machine Learning to your own data for research and analysis. This course aims to build your skills so you can use these models responsibly.
Prerequisites
-
We assume you are familiar with the modules introduced in the Python for Data-analysis training, being Panda’s Numpy, Matplotlib and Seaborn.
-
If you have prior experience but would like to get a refresher on these modules, we are planning to create a few practice assignments. You can get on-site support at the Research Support Hub in the weeks leading up to the Machine Learning training.
-
Please note: Machine Learning has a steep learning curve. Expect you will need 2-4 hours between sessions to prepare for the next one and plan accordingly. Please be kind to yourself and allow yourself at least 48 hours to complete the course.
Interactive Learning Experience
This course relies heavily on highly interactive (online or hybrid) sessions where we review what you have done in the reader. During a review the teacher will share his screen and go through the code asking you by voting and chatting to find the error or complete the code. These reviews are used to rehearse material, show tips and tricks, warn for common mistakes, explain error messages, show how to use the helpfiles and the programme (IDE) in general, and overall to motivate you to keep up the pace. Participants report they are highly involved during these sessions and our courses are consistently highly evaluated.
Date, time and location
Below are the dates and times of the next Machine Learning using Python (s). The content of these courses is the same each time. If these dates do not suit you and you want to be notified on future courses please mail the coure coordinator (Theo van Mourik, t.j.van.mourik rug.nl).
Febuary 2026 – 3 week course
-
All sessions take 4 hours and take 3-4 hours of preparation each.
-
All presentations will be recorded and recordings will be available for about 6 months after the course.
-
You’ll receive a certificate of attendance for attending 5 of 6 sessions or (if you prefer) after completing a final assignment.
|
Session
|
Date (9 a.m. to 1 p.m.)
|
Preparation
|
|
1
|
Tue 3 Feb
|
TBA
|
|
2
|
Fri 6 Feb
|
TBA
|
|
3
|
Tue 10 Feb
|
TBA
|
|
4
|
Fri 13 Feb
|
TBA
|
|
5
|
Tue 17 Feb
|
TBA
|
|
6
|
Fri 20 Feb
|
TBA
|
June 2026 - 3 week course
-
All sessions take 4 hours and take 3-4 hours of preparation each.
-
All presentations will be recorded and recordings will be available for about 6 months after the course.
-
You’ll receive a certificate of attendance for attending 5 of 6 sessions or (if you prefer) after completing a final assignment.
|
Session
|
Date (9 a.m. to 1 p.m.)
|
Preparation
|
|
1
|
Tue 16 June
|
TBA
|
|
2
|
Fri 19 June
|
TBA
|
|
3
|
Tue 23 June
|
TBA
|
|
4
|
Fri 26 June
|
TBA
|
|
5
|
Tue 30 June
|
TBA
|
|
6
|
Fri 3 July
|
TBA
|
If you want to be notified on future courses in Python (or R) please mail the coordinator. (Theo van Mourik, t.j.van.mourik rug.nl)
Enrollment and course fee
Late enrollments are fine, but please also contact the coordinator to not risk being overlooked. You can unenroll until 8 days prior to the first session. The participants will be mailed the course material a few days prior to the course. For more info, you can mail Theo van Mourik (t.j.van.mourik rug.nl ).
Prices for individuals:
-
€100 BSc/MSc student at UG, other Dutch University or Hanze Hogeschool
-
€300 PhD-student at UG or other Dutch University
-
€650 Employee UG or other Dutch university/UMCG/Hanze Hogeschool
-
€650 UG Alumni
-
€1250 Other participants
Prices for groups joining the course:
-
20 or more PhD’s €200pp (minimum price of €4.000)
-
10 or more Employees €400 pp (minimum price of €4.000)
-
20 or more Employees €250 pp (minimum price of €5.000)
-
5 or more others €1.000 pp (minimum price of €5.000, we will also send a separate contract for this)
When enrolling a group of participants, you need to report a single financial contact person/ cost centre and the mailing addresses of all participants. You can enroll a group with the normal enrollment link.
You can also order a custom course and discuss dates, audience, and content. For more information mail Theo van Mourik (t.j.van.mourik@rug.nl). This is also possible within the curriculum.
More information
For more information on this or similar courses, please mail the coordinator, Theo van Mourik (t.j.van.mourik rug.nl)