In a VUCA world, which is becoming increasingly volatile, uncertain, and complex, companies, organizations, and states must respond promptly and adequately to the respective situations. Making decisions based on past experiences is less successful in these times than having an accurate understanding of current conditions. The importance of empirical sciences, continuous environmental observation, timely analysis of causal relationships, and deriving new insights from them is increasing. From this, it can be deduced which measures are likely to achieve one's goals with predictable probability, such as which price for an offer generates the desired demand or which marketing measure reaches the desired target group.

Where classical statistics were once used for calculations and predictions, today free (open source) tools like R allow data in various formats and from any number of sources to be read in, processed, and analyzed using methods of Artificial Intelligence and Machine Learning. The results can then be perfectly visualized so that decision-makers can benefit quickly and effectively.

The age of Data Science has arrived. Digitalization is more than a buzzword or a promise; it is actionable and usable for everyone.

This book teaches you, based on the latest version of R at the time of publication, how to use Artificial Intelligence and Machine Learning in Industry 4.0.

Conveys applications of AI and Machine Learning Uses data from business information systems and social networks Ideal for students and other individuals who want to acquire knowledge in AI and Machine Learning

Autorentext

Bernd Heesen is a professor at the Faculty of Economics at Ansbach University of Applied Sciences in Bavaria. Before his tenure at the university, he worked for more than 10 years as a business consultant both domestically and internationally. He continues to advise companies on the use of IT innovations.

Inhalt

Benefits of Machine Learning and Artificial Intelligence.- Machine Learning.- Best Practices.- Setting Up the R Development Environment.- Fundamentals of the R Programming Language.- Machine Learning with R.- Application of Machine Learning with R.- Outlook.