The basic training in Python with the focus on data science is the ideal introduction for analysts to the programming language. In addition to programming paradigms, object types and syntax structures and pandas, the central library for data management. Through the training the participants will be able to implement the data management as well as the first analyses and visualizations in Python.

Course content:

• First steps into Python
• Concept and philosophy of Python
• Data structures and their properties
• Importing data
• Data management with pandas
• Data analysis with Python
• Loops and control elements

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Recommended course length: Two days

A selection of the training contents:

• Introduction to the basic concepts of machine learning
• Dealing with the machine learning framework scikit-learn
• Introduction to machine learning algorithms such as decision trees, support vector machines or random forests
• Creation of training and test data
• Parameter tuning of the models with the help of cross-validations
• Presentation of relevant processing steps such as one-hot encoding, standardization or imputation
• Presentation of different metrics of model evaluation
  • For classifications: (Balanced) Accuracy, Sensitivity, Specificity, Area under the curve
  • For regressions: RMSE, MAE
• Linking of preparation and modeling steps in pipeline objects

The course is aimed at people who have already had some programming experience with Python and have a basic understanding of statistics.

Recommended course length: Two days

Transformer models have revolutionized the way computers process and understand texts. They are at the heart of modern AI applications. In our training session, we take a look at the background and use cases of transformer models and teach the skills for their practical application.

Course content:

• Introduction: Presentation of the use cases of Transformer models (text, audio, image)
• Background of Transformer models
• Practical work with transformers in Huggingface
• What is Huggingface and what do I need it for?
• How do I process data and prepare it for working with transformers? (datasets library)
• How do I apply Transformer models in different use cases? (transformers library)
• How do I evaluate the performance of the models? (evaluate library)
• How can I fine-tune a pre-trained model for my use case?

Recommended course length: Two days

Optionally, we can extend our training on the topic of transformer models by a further day to a total length of three days.

The course content of the extension day at a glance:

• Transformer in production: Presentation of a RAG system
• Improving speed and reducing RAM consumption with quantization and the accelerate library
• Which role do transformers play in a RAG system?
• How do I set up a RAG system: langchain library?
• Prompt Engineering
• (RAG as an app with gradio)