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Machine learning with Python

Individual course

Max amount of FITech students: 1 000

Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.

Our every-day lives are substantially affected by political decisions. These decisions are often based on the predictions of models that have been fit to data. As a case in point, consider school closures and other non-pharmaceutical interventions during the Covid-19 pandemic. These interventions have been decided by policymakers based on the predictions obtained from fitting epidemiological models to healthcare data.

This online course offers a hands-on introduction to some widely used methods in machine learning (ML). ML revolves around computationally efficient methods to fit high-dimensional models (such as deep neural networks) to large amounts of training data (such as all the public health-care data collected during the Covid-19 pandemic).

Students will learn how to apply ready-made ML methods in the programming language Python to “real-world” problems. You will learn to gather data from different online sources, use it to train state-of-the are ML models and critically assess the predictions delivered by the trained model.

Teaching methods

The main component of the course are coding assignments that require students to complete prepared Jupyter notebooks. These notebooks combine Python code snippets with textual explanations of the code. To support students with completing the coding assignments, we will offer exercise sessions. These sessions will be recorded and made available during the course.

The course also includes a project that requires students to solve (“toy”) machine learning problem and to document the problem and its solution by a report in the form of a Python notebook.


Students can complete the course according to their own preferred schedule. The deadlines for graded activities (such as the coding assignments) will be announced at the beginning of the course.

Completion methods

The grading will be based entirely on the coding assignments and the student project.

For Aalto degree students: The content of this course overlaps with CS-E3210 Machine learning: basic principles. Both courses cannot be included into degrees.

More information in the Aalto University study guide.

You can get a digital badge after completing this course.

Learn on the video how machine learning can transform our lives!

koodaus, koodaaminen, koneoppiminen, regressio, klusterointi, luokittelu

Responsible teachers

Aalto University
Alex Jung, Assistant professor
Aalto University
Shamsiiat Abdurakhmanova

Further information about the studies

Aalto University
Tiina Porthén

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
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AI and machine learning,
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0 €
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Host university:
Aalto University
Who can apply:
Adult learner,
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General prerequisites:
Basic knowledge of mathematics (functions, vectors and matrices) and basic programming skills in any high-level programming language (e.g. Python).
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