Artificial Intelligence - Development and Data Analysis

Course overview

Goals of the Training The focus of the training in Artificial Intelligence - Development and Data Analysis is on understanding and applying techniques of artificial intelligence and data analysis. The content includes both the fundamentals of artificial intelligence and specialized methods of machine learning, including Supervised and Unsupervised Learning as well as Feature Engineering. Neural networks and Deep Learning are also covered to provide deeper insights into complex data structures. Advanced data analysis techniques will be taught to make informed decisions based on data. A practical project in data analysis rounds off the training and offers the opportunity to apply what has been learned in real-world scenarios. The training requires a basic understanding of mathematical analysis, linear algebra, and statistics, as well as prior knowledge in Python, common machine learning libraries, and data analysis. In summary, it provides a foundation for anyone looking to advance in the field of artificial intelligence, development, and data analysis. Content of the Training Artificial Intelligence History of AI Modern AI Systems Reinforcement Learning Natural Language Processing (NLP) Computer Vision Machine Learning - Supervised Learning Introduction to Machine Learning Regression Basic Classification Techniques Support Vector Machines Decision Trees and Regression Trees Machine Learning - Unsupervised Learning and Feature Engineering Introduction to Unsupervised Learning and Feature Engineering Clustering Dimensionality Reduction Feature Engineering Feature Selection Automated Feature Generation Neural Networks and Deep Learning Introduction to Neural Networks Feed-Forward Networks Avoiding Overfitting Convolutional Neural Networks Recurrent Neural Networks Advanced Data Analysis Analysis of Business Performance Text Mining Web and Social Media Analysis Experimentation and Testing Project: Data Analysis Transfer of methodological knowledge to implement analytical use cases in the real world from the aforementioned problem areas.

Upcoming dates

• 2026-09-17T22:00:00.000Z · 2027-03-17T23:00:00.000Z · Karlsruhe, Baden · Blended Learning
• 2026-09-17T22:00:00.000Z · 2027-03-17T23:00:00.000Z · Duisburg · Blended Learning
• 2026-09-17T22:00:00.000Z · 2027-03-17T23:00:00.000Z · Leinfelden-Echterdingen · Blended Learning
• 2026-09-17T22:00:00.000Z · 2027-03-17T23:00:00.000Z · Nürnberg, Mittelfranken · Blended Learning
• 2026-09-17T22:00:00.000Z · 2027-03-17T23:00:00.000Z · München · Blended Learning