Introduction to Machine Learning with R

COURSE DESCRIPTION
In this course, we give you an insight into machine learning algorithms and show you how to develop your own models, what challenges you might face and how to master them. The course deals with the following topics:

• Introduction to the basic terms of machine learning
• Introduction to machine learning algorithms such as decision trees, random forest, gradient boosting machine
• Introduction to a methodical approach in the development of machine learning models
• Typical steps in data preparation such as feature selection or data transformation
• Creation of training and test data sets
• Introduction of validation techniques such as cross-validation or bootstrapping
• Introduction and interpretation of different metrics for measuring success such as:
• For classifications: Accuracy, sensitivity, specificity
• For regression: RMSE, MAE, MAPE, …
• Interpretation of ROC curves
• Tuning of parameters
• Introduction of the data mining framework caret

OBJECTIVES
Based on practical examples and exercises, we teach you the skills to implement machine learning methods in R independently. The preparation of data, the development and training of algorithms and the validation of analysis models: In our course you will get to know the central steps of machine learning.

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 R is a prerequisite for productive participation. This means that the basic data types are known, and classes, functions and methods can be safely distinguished. R beginners should participate in our Introduction to R for Data Science course.