The book "Information-Driven Machine Learning Data Science as an Engineering Discipline" presents a comprehensive overview of the field of machine learning and data science, highlighting its importance and relevance in today's technology-driven world. The author emphasizes the need for a personal paradigm for understanding the technological process of developing modern knowledge, which can serve as the foundation for the survival of humanity and the unity of people in a divided world. The book begins by exploring the concept of information and its role in shaping our understanding of the world. It discusses the various sources of information, including sensory input, cognitive biases, and social influences, and how they impact our perception of reality. The author then delves into the history of machine learning and data science, tracing their evolution from simple statistical models to complex neural networks and deep learning algorithms. The book also examines the current state of the field, including the challenges and opportunities presented by big data, artificial intelligence, and the Internet of Things (IoT). It highlights the need for interdisciplinary collaboration between computer scientists, mathematicians, statisticians, and domain experts to develop effective machine learning models that can solve real-world problems. Furthermore, the book emphasizes the importance of ethical considerations in machine learning and data science, such as privacy, bias, and explainability.

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