Bitte beachten Sie, dass der Workshop in englischer Sprache stattfinden wird.
COURSE DESCRIPTION
In this training, the participants gain an overview of the topic machine learning and learn to apply ML-methods with the programming language Python for their own questions. The central topics of the training are classification and regression tasks as well as cluster analysis.
In addition to the algorithms themselves, the training tries to give an impression of machine learning processes. It will be demonstrated what steps are necessary to solve a machine learning task and how they are implemented in concrete terms. Thus, not only the algorithm / method becomes topic of discussion, but also further aspects such as data pre-processing, model requirements and the interpretation of the results.
OBJECTIVES
- Introduction to the methodological basics of machine learning
- Introduction to basic machine learning techniques with Python
- Model creation and model evaluation with Python
TARGET AUDIENCE
This course is intended for people who are interested in the field of data science or want to expand their knowledge of the subject area “Machine Learning”. Prior knowledge of Python is a prerequisite for productive participation. This means that the basic data types are known, and classes, functions and methods can be safely distinguished.
SOFTWARE REQUIREMENTS
- Python (Anaconda-Distribution) in current Version (the development environments 'Jupyter Notebooks' and 'Spyder' should be set up; Python should be entered in the environment variables of the operating system – this can already be done during the installation)
Download: www.anaconda.com/distribution/
- PyCharm in current version (Community-Version)
Download: www.jetbrains.com/pycharm/download/#section=windows
ABOUT THE TRAINER
Andreas Wygrabek is a freelance data science expert and experienced trainer in programming and statistics with a career background in an IT consultancy. With his project
data-science-architect he is offering data science services for industry and academic institutions. In his projects he is revealing insights from data through the use of modern algorithms and visualization techniques. The toolset he uses covers the most popular programming languages in the field of data science – R and Python.