This repo contains the course material for the Machine Learning – Tools and applications for policy course. The course material consists of the slides in the lecture notes folder and the notebooks in the tutorials folder. The tuotiral will provide the hands-on experience with some machine learning, while the slides would give the theoretical background, so you also know what you are doing. The hands-on expierence is important because an essential part of Machine Learning is just trying things until they work.
For the tutorials you can have a look at Getting Started — scikit-learn 1.3.2 documentation to see what the syntax roughly looks like. In addition, it is useful if you know something about data manipulation (i.e. pandas - Python Data Analysis Library). We are going to offer all this in Jupyter Notebooks with which we hope to make it as user-friendly as possible.
The basic training we offer at DNB for Python is through Datacamp: Introduction to Python Course. The advantage is that you don't need to have Python installed for this. Through DNB academy you can also follow a more advanced course: Fundamentals of python modeling in finance. In the slides for Lecture 1 there are several (online) textbooks listed that you might find of interest.