An Introductory Course to Scientific Computing & Machine Learning in Python
This repository contains all the files (slides, source code & data) used in the first two days of the Scientific Python Course at TU Braunschweig.
Here you can find the material of the 3rd day which is on Research Software Engineering in Python and also covers Object-Oriented Aspects in Python that this course does not contain.
The target audience is someone with a bit of prior programming knowledge, but in a different language like MATLAB. It is helpful to understand some linear algebra in order to follow the examples in the sections on Machine Learning & Deep Learning.
The course is supposed to be interactive for people to code along the instructor.
Do you like this? Then you might also enjoy my YouTube channel on Machine Learning & Simulation for which all material is also available in the corresponding GitHub Repo.
Copyrights of the datasets
- 19th_bundestag_example.csv, 19th_bundestag_example.ods, election_2017_parties.csv, election_2017_results.csv : Bundeswahlleiter
- age_to_getting_pesion.csv : Artifical dataset created from sampling a Logistic Regression with added noise
- msci_world_monthly.csv : End of Month course value taken from MCSI Homepage
- takeover_trajectories.csv.gz : Private