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Machine Learning (ML) is a branch of artificial intelligence that allows computers to learn from data without explicit programming. ML is applied in various fields, including education, pattern recognition, industries, social media, and product recommendations. In education, ML and Educational Data Mining (EDM) are becoming crucial due to the growing amount of student data. EDM helps uncover hidden information in educational datasets, aiding in student performance improvement and better decision-making for teachers and institutions. Techniques like clustering (e.g., modified K-means) and classification (e.g., decision trees) are commonly used to analyze student performance. Clustering groups students based on characteristics, while the Elbow method helps determine optimal cluster size. ML in education helps enhance student outcomes and optimize administrative decisions, making it valuable for both students and institutions. This data-driven approach is key to improving education quality in the future.
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Unlocking Student Insights: Performance Analysis with Enhanced K-Means
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Unlocking Student Insights: Performance Analysis with Enhanced K-Means
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Unlocking Student Insights: Performance Analysis with Enhanced K-Means
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Unlocking Student Insights: Performance Analysis with Enhanced K-Means
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Unlocking Student Insights: Performance Analysis with Enhanced K-Means
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Unlocking Student Insights: Performance Analysis with Enhanced K-Means
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Unlocking Student Insights: Performance Analysis with Enhanced K-Means
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Unlocking Student Insights (Paperback)
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Paperback. Zustand: new. Paperback. Machine Learning (ML) is a branch of artificial intelligence that allows computers to learn from data without explicit programming. ML is applied in various fields, including education, pattern recognition, industries, social media, and product recommendations. In education, ML and Educational Data Mining (EDM) are becoming crucial due to the growing amount of student data. EDM helps uncover hidden information in educational datasets, aiding in student performance improvement and better decision-making for teachers and institutions. Techniques like clustering (e.g., modified K-means) and classification (e.g., decision trees) are commonly used to analyze student performance. Clustering groups students based on characteristics, while the Elbow method helps determine optimal cluster size. ML in education helps enhance student outcomes and optimize administrative decisions, making it valuable for both students and institutions. This data-driven approach is key to improving education quality in the future. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9786208172374
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Unlocking Student Insights: Performance Analysis with Enhanced K-Means
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Taschenbuch. Zustand: Neu. Neuware -Machine Learning (ML) is a branch of artificial intelligence that allows computers to learn from data without explicit programming. ML is applied in various fields, including education, pattern recognition, industries, social media, and product recommendations. In education, ML and Educational Data Mining (EDM) are becoming crucial due to the growing amount of student data. EDM helps uncover hidden information in educational datasets, aiding in student performance improvement and better decision-making for teachers and institutions. Techniques like clustering (e.g., modified K-means) and classification (e.g., decision trees) are commonly used to analyze student performance. Clustering groups students based on characteristics, while the Elbow method helps determine optimal cluster size. ML in education helps enhance student outcomes and optimize administrative decisions, making it valuable for both students and institutions. This data-driven approach is key to improving education quality in the future.Books on Demand GmbH, Überseering 33, 22297 Hamburg 84 pp. Englisch. Bestandsnummer des Verkäufers 9786208172374
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Unlocking Student Insights: Performance Analysis with Enhanced K-Means
Verlag:
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6208172373
ISBN 13: 9786208172374
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning (ML) is a branch of artificial intelligence that allows computers to learn from data without explicit programming. ML is applied in various fields, including education, pattern recognition, industries, social media, and product recommendations. In education, ML and Educational Data Mining (EDM) are becoming crucial due to the growing amount of student data. EDM helps uncover hidden information in educational datasets, aiding in student performance improvement and better decision-making for teachers and institutions. Techniques like clustering (e.g., modified K-means) and classification (e.g., decision trees) are commonly used to analyze student performance. Clustering groups students based on characteristics, while the Elbow method helps determine optimal cluster size. ML in education helps enhance student outcomes and optimize administrative decisions, making it valuable for both students and institutions. This data-driven approach is key to improving education quality in the future. Bestandsnummer des Verkäufers 9786208172374
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