Machine Learning

Data Science and Artificial Intelligence


Today, the topics data science, artificial intelligence, and machine learning constitute the backbone of any IoT or digitization solution, as they are actually responsible for value creation. This training provides an insight as to with which methods and technologies digital data can be processed, analyzed and used for a self-learning business optimization. The course is designed according to the requirements of the industry and comes with many interactive exercises, which have been worked out for both the direct user and the decision-taker. This permits a profound and comprehensive introduction into the areas of artificial intelligence and machine learning. For this purpose, the training imparts the required core competence for the generation or evaluation of existing concepts and creates the relation to practical application.


Course Contents

• Introduction to Data Science, Machine Learning, and Artificial Intelligence
• Basics of Python Regarding Data Processing, Statistics, and Data Virtualization
• Machine Learning Workflow
• Learning Scenarios and their Fields of Application (e.g. Predictive Analytics, Bots, Recommendation Services)
• Machine Learning Methods in Comparison (from Decision Tree up to Deep Learning)

  • Artificial Neuronal Networks
  • Decision Trees
  • Support Vector Machines
  • Clustering

• Evaluating and Validating Models Correctly
• Overview of Software and Tools
• Application Scenarios (What is a hype and where is potential?)
• Questions and Fears in a Social Context
• Consistent Interactive Hands-On Exercises

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Target Group


The course is offered for IT-oriented employees (from user to decision-taker) looking for an introduction to the topics data science, artificial intelligence, and machine learning, meant for practical application.


Knowledge Prerequisites

The students should have a basic understanding of and interest in the trend topics digitalization, Big Data, and IoT. Basic programming know-how would be advantageous.