From the perspective of technical occupational safety and health (OSH), the safety-related assessment of systems capable of learning requires a more in-depth technical introduction to the world of machine learning (ML) as a subfield of artificial intelligence (AI). To this end, OSH stakeholders should familiarize themselves with the basic modes of operation of typical ML algorithms, appropriate software tools, libraries and programming systems. Therefore, this Getting Started Tutorial Step-by-step_intro_to_ML_with_SVC_and_Iris.ipynb in the form of a Jupyter notebook aims to demonstrate systematically and step-by-step the typical ML workflow using the very powerful and performant Support Vector Classifier (SVC) as an example. The process steps of data analysis and classification are illustrated by using the widely known and remarkably beginner-friendly Iris dataset. In addition, the selection of the correct SVC kernel and its parameters are described, and their effects on the classification result are shown. In November 2022, the Artificial Intelligence Conference took place in Dresden, which was hosted by the German Social Accident Insurance (DGUV). There, the current tutorial was presented to interested ML newcomers in the technical occupational safety and health of the social accident insurance institutions as part of a separate Getting Started Workshop. Github-Repository: https://github.com/urmel79/Jupyter_GettingStarted2ML