An online course with 10 lectures offering a practical introduction to machine learning, including classification, regression, dimensionality reduction, and unsupervised learning. Topics covered include linear classification and regression, nearest neighbor methods, support vector machines, decision trees, neural networks, clustering, and anomaly detection.
The course has 10 lectures focusing on different methods and algorithms. The lectures consist of short videos, articles, and hands-on exercises using Python with Scikit-Learn and other relevant machine learning libraries. CSC's Notebooks environment (or Google Colaboratory) will be used in the exercise sessions. This means that you don't have to install anything on your own computer to do the exercises.
After the course, you should have the skills and knowledge needed to begin applying machine learning for different tasks.
We assume you have a basic knowledge of Python and Numpy. While this course takes a practical approach to machine learning, there are still some mathematics and theoretical considerations. Knowledge of the basics of linear algebra, probability theory, and multivariate calculus can be helpful.
To self-enroll in this MOOC course, please visit our eLena page.
To self-enroll you can use your HAKA credentials, if you do not have one please contact firstname.lastname@example.org to receive credentials.