Machine Learning with Python

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.
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Schedule:

Teaching time:

Self-paced

Location:

Online

Topic:

Information and communications technology

Form of learning:

Online

Provider:

Aalto University, FITech

Level:

Intermediate

Credits:

2 By Aalto University (ECTS)

Fee:

Free of charge

Target group and prerequisites

Basic knowledge of mathematics (functions, vectors and matrices) and basic programming skills in any high-level programming language (e.g. Python).

Course description

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.

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.

Application deadline: 2.1.2022.

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