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The aim of this book is to provide insight into Data Science and Artificial Learning Techniques based on Industry 4.0, conveys how Machine;
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Master the approaches and principles of Artificial Intelligence (AI) algorithms, and apply them to Data Science projects with Python and;
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Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast;
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. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications;
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is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework.The following;
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big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven;
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This book provides an overview of current issues and challenges in the fashion industry and an update on data-driven artificial;
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. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained;
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value migration in an industry and business models. Section 4 provides a taxonomy of the new business models that have emerged as a result of AI;
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methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations;
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methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations;
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contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning;
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programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book;
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researchers and students in agricultural economics, data science, engineering, and machine learning as well as engineers and industry professionals;
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about the world and what it brings to our lives. Two new co-authors have introduced a wealth of recent data and applications to keep the science;
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contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning;
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-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity;
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, Mathematics, and Engineering 3 Meeting #2: Examining the Intersection of Domain Expertise and Data Science 4 Meeting #3: Data Science Education;
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mining, visual data analytics, AI in molecular biology, DNA, RNA, proteins, big data analytics, Internet of Things and recommender systems, and;
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. Chapter 1 begins with a brief introduction to AI and describes its relationship with machine learning, data science and big data analytics;
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book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an;
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book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an;
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Bulgaria 1 1 0. 5 0. 5 New Zealand 4 4 1 1 Canada 13 4 4. 75 2 Norway 4 1 2. 33 1 Chile 14 10 6 4 Poland 8 2 3 1 China 288 65 107. 33 23. 66;
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industry, and can be successfully deployed by business executives, data science innovators and practitioners. Referencing multi-cloud Agile DevOPs;
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This book comprises select proceedings of the International Conference on Emerging Trends for Smart Grid Automation and Industry 4.0;
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This book presents selected papers from the 1st International Conference on Industry 4.0 and Advanced Manufacturing held at the Indian;
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Learn how AI and data science are upending the worlds of biology and medicine In Silico Dreams: How Artificial Intelligence and;
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