Machine learning with Python

Max amount of FITech students: 1000

Persons without a valid study right to a Finnish university have preference to this course.

This online course provides a hands-on introduction to some widely-used methods in machine learning (ML). Students will learn how to apply ready-made ML methods in the programming language Python to particular (“real-world”) problems.

Our every-day lives can be substantially affected by political decisions. Some of these decisions are based on predictions obtained by fitting statistical models to data. As a point in case, consider school closures that are decided based on predictions obtained from fitting epidemiological models to healthcare data.

This online course teaches you how to use the programming language Python to gather data from different online sources and how to fit simple models, such as linear or decision tree models, to this data.

The course consists of coding assignments that require students to complete ready-made

Python notebooks (which combine Python code snippets with textual explanations of the code).

Students can also choose from a set of mini-projects that require to solve small data analysis tasks and to prepare a report in the form of a Python notebook.

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

Schedule:

No exam. No particular schedule except for the deadlines for course exercises.

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

You can get a digital badge after completing this course.

More information in Aalto University’s WebOodi course page.

Student feedback from earlier implementations of the course:

• “Finally a practical hands-on course instead of heavy theory loading and little practical understanding!”
• “The Slack discussion forum was very helpful to get tips if you got stuck on some assignment, the course staff was active there.”
• “The teacher is really committed and supportive. He can explain/present things clearly even on an online course.”
• “Jupyter notebook as a teaching platform was excellent”
• “Exercises were very good. Liked that the exercises got more challenging towards the end of the course.”

Read about Aira’s experiences of Machine Learning with Python course here!

koodaus, koodaaminen, koneoppiminen, regressio, klusterointi, luokittelu