Textbook in PDF format

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 (AI) and Machine Learning (ML). 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.
With this book, I want to contribute to presenting what “Machine Learning” and “Artificial” Intelligence actually are and where their limits lie. Of course, I also want to present where and in what way they can be beneficial. In particular, I introduce the three types of Machine Learning, Supervised, Unsupervised, and Reinforcement Learning, and selected, widely used algorithms, which form the basis for Artificial Intelligence. Ultimately, Machine Learning can only function effectively if the underlying data for the learning process is of sufficient quality. Therefore, I also present best practices of Data Science, including data preprocessing and visualization.
The datasets used in this book are provided to you through my R-package “Machine Learning”, so that you can execute the code examples presented in the book yourself. The package also includes interactive self-learning tutorials for learning the R programming language.
Benefits of Machine Learning and Artificial Intelligence
Machine Learning
Best Practices
Setting up the R Development Environment
Basics of the R Programming Language
Basics of Machine Learning with R
Application of Machine Learning with R
Outlook