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This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanation of key AI technologies: machine learning, deep learning, evolutionary computation, cognitive computing, swarm intelligence, decision trees, and intelligent agents. In Part II, he describes the main tools and processes of the AI-Based Data Science toolbox such as problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also recommends how to develop data science solutions in a business setting and provides guidelines for building and leveraging skills in business-related applications. The book is ideal for data scientists who will implement the proposed methodologies and techniques in their projects. It is also intended to help managers and entrepreneurs who want to create competitive advantage by using data science, as well as academics and students looking for an industrial view of this discipline.
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