Machine Learning – Predicting properties on Spotify data

Machine learning uses algorithms to learn patterns and regularities in data. Such statistical models are suitable for predicting events. The participants practically work out the creation of a model for regression analysis using a continuous data example (Spotify data). Regression analysis is one of the statistical analysis techniques. It models relationships between a dependent variable and one or more independent variables. The online service Spotify stores audio properties, e.g., energy and volume, for musical works. In the course, participants will use the model created to examine pieces of music and determine properties predictively.

Details

Titel: Machine Learning – Predicting properties on Spotify data
Next Session:
06.09.2022, 3 p.m. – 6 p.m. (Speakers: Anja Neumann, Timo Adameit, Jan Ewald, Thomas Burghardt)
Registration: https://event.zih.tu-dresden.de/nhr/regression
Target group: Beginner on Python, basic knowledge on Pandas
Language: German
Format: Tutorial, hybrid

Agenda

Handouts

The following handouts (slides, example applications) will be provided to the participants:

Prerequisites

Learning outcomes

After the training, participants will know and be able to use the regression model selected by the trainees to predict values.

Contact

Check out the other trainings by ScaDS.AI Dresden/Leipzig.

TU
Universität
Max
Leibnitz-Institut
Helmholtz
Hemholtz
Institut
Fraunhofer-Institut
Fraunhofer-Institut
Max-Planck-Institut
Institute
Max-Plank-Institut