Introduction to machine learning with Sklearn

• Machine learning: We start with a basic introduction to the terms and concepts of machine learning.

• Types of machine learning: You will get an overview of the different types of machine learning, including supervised and unsupervised learning, as well as the methods of classification and regression.

• Workflow of a machine learning project: We present the typical workflow of a project from data collection to model selection and model evaluation.

• Exploratory data analysis: You will gain an initial overview of your data and recognise important correlations.

• Data preparation: Typical steps are explained, from dealing with erroneous values to standardisation.

• Training: You will become familiar with the phenomenon of overfitting and the methods of cross-validation and regularisation.

• Quality measures: You will learn how to assess the quality of a created model.

• Feature selection: The influence of the selection of features used is clarified and strategies for feature selection are presented.

• Python frameworks: We will introduce you to Python frameworks for machine learning, including Sklearn, Pandas and NumPy.

Duration of the course: 2 days

With this course you will lay a solid foundation for your further involvement with machine learning and will be able to start your own projects in this area.