Machine learning with Python

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 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 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.

The 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.

Want to know more? Read about Aira’s experiences here!

Student feedback:

• “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.”

Teaching methods

The course includes some online lectures that will be recorded and available online during the course. The material will be provided on the course page.

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.

Schedule

No particular schedule except for the deadlines for course exercises (will be specified at the beginning of the course).

Completion methods

The grading will be based entirely on the coding assignments and student projects. No exam.

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.

koodaus, koodaaminen, koneoppiminen, regressio, klusterointi, luokittelu