Max amount of FITech students: 25 adult learners
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 optimised pipeline leading from data to knowledge. The course introduces many machine learning algorithms and discusses their advantages and limitations. Methods for data and model visualisation and reporting are utilised throughout the course.
Course contents
- 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
Learning outcomes
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.
Course material
VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data (1 edition).
Teaching schedule
The course does not have a fixed schedule because it is based on recorded video material. The course schedule will be available in the study guide (see link below).
Completion methods
The course evaluation is based on quizzes, weekly exercises and a machine learning project.
More information in the University of Vaasa study guide.
You can get a digital badge after completing this course.