Applied machine learning
Min amount of FITech students: 4
Max amount of FITech students: 30 (including max. 5 degree students)
This course provides an insight into the implementation of machine learning for applications. The course covers methods needed for data analysis from reading and cleaning the data, imputation of missing values, extracting features and application of machine learning methods to develop an optimized pipeline leading from data to knowledge. The course introduces many machine learning algorithms and discusses their advantages and limitations. Methods for data and model visualization and reporting are utilized throughout the course.
- Introduction to machine learning
- Introducing Python
- Reading and cleaning data and plotting
- Preprocessing and feature extraction
- Unsupervised ML for data exploration
- Supervised machine learning
- Evaluation and optimisation of the models
Students who complete this course successfully will be aware of the practical implementation and usage of machine learning algorithms. Furthermore, they will be able to apply machine learning algorithms in real problems using efficient programming languages, for example Python.
VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data (1 edition).
The course evaluation is based on quizzes, weekly exercises and a machine learning project.
You can get a digital badge after completing this course.
Contact person for applications