Machine Learning using Python

When: 30 June - 11 July 2025
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'll explore the different types of algorithms, their uses, and limitations, and you'll also touch on the most advanced, but computationally demanding vision and language algorithms.
✅ You'll learn how to preprocess data, select models, and evaluate performance, with a focus on hands-on examples.
✅ You'll work with algorithms for regression, classification, and clustering, as well as delve into neural networks and deep learning.
✅ Throughout the course, you’ll build practical skills in Python using libraries like scikit-learn, pandas, and TensorFlow.
✅ You’ll also tackle challenges such as overfitting, feature selection, and hyperparameter tuning.

Result

By the end of the course, you’ll 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.

• For if you have prior experience but would like to get a refresher on these modules, we’re 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’ll 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’ve 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 program (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.

More information

For more information on this or similar courses, please mail the coordinator, Theo van Mourik (t.j.van.mourik rug.nl)