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Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add;
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Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil;
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Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs;
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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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and industry practitioners who want to keep abreast of the latest developments in this dynamic, exciting and interesting research field;
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with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive;
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with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive;
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The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning;
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Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their;
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in the industry, and help drive the transformation of organizations into a knowledge and data-driven entity. It provides an end-to-end;
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insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming;
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Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches;
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learning on the cloud Who This Book Is For This book is intended for data scientists and managers of predictive analytics projects; it will;
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in the industry, and help drive the transformation of organizations into a knowledge and data-driven entity. It provides an end-to-end;
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Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the;
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Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the;
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Teaches the machine learning process for business students and professionals using automated machine learning, a new development in data;
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Teaches the machine learning process for business students and professionals using automated machine learning, a new development in data;
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Learn * Utilize the power of R to handle data extraction, manipulation, and exploration techniques * Use R to visualize data spread across;
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Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Tackle data science;
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This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data;
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This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data;
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publication is designed for IT specialists, computer engineers, researchers, academicians, and industry professionals interested in the impact;
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* Understand the key frameworks in data science, machine learning, and deep learning * Harness the power of the latest Python open source libraries;
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learning (QML) through various options available in industry and research. The first three chapters offer insights into the combination of the;
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generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted;
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This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale;
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