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Synopsis
Pharmaceutical science is entering a new era in which data can reveal patterns, predict outcomes, and guide decisions that conventional analysis may overlook. Introduction to Machine Learning in Pharmaceutical Sciences provides a clear and practical pathway into this rapidly evolving field, connecting computational principles with real pharmaceutical challenges.Beginning with the foundations of artificial intelligence, machine learning, and data science, the book explains pharmaceutical datasets, data preparation, regression, classification, clustering, decision trees, and model evaluation in an accessible yet scientifically rigorous manner. Application-focused discussions demonstrate how these methods support drug discovery, formulation development, quality control, manufacturing, pharmacovigilance, and clinical decision-making.Designed for pharmacy students, teachers, researchers, and early-career pharmaceutical professionals, the book bridges the gap between theoretical algorithms and their responsible use in pharmaceutical research and healthcare. Readers gain the conceptual foundation needed to interpret models critically, assess the reliability of predictions, and convert complex data into meaningful scientific insight.For anyone seeking to understand how machine learning is reshaping pharmaceutical sciences, this book offers an authoritative and accessible starting point.
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Introduction to Machine Learning in Pharmaceutical Sciences
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Introduction to Machine Learning in Pharmaceutical Sciences (Hardcover)
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Hardcover. Etat : new. Hardcover. Pharmaceutical science is entering a new era in which data can reveal patterns, predict outcomes, and guide decisions that conventional analysis may overlook. Introduction to Machine Learning in Pharmaceutical Sciences provides a clear and practical pathway into this rapidly evolving field, connecting computational principles with real pharmaceutical challenges.Beginning with the foundations of artificial intelligence, machine learning, and data science, the book explains pharmaceutical datasets, data preparation, regression, classification, clustering, decision trees, and model evaluation in an accessible yet scientifically rigorous manner. Application-focused discussions demonstrate how these methods support drug discovery, formulation development, quality control, manufacturing, pharmacovigilance, and clinical decision-making.Designed for pharmacy students, teachers, researchers, and early-career pharmaceutical professionals, the book bridges the gap between theoretical algorithms and their responsible use in pharmaceutical research and healthcare. Readers gain the conceptual foundation needed to interpret models critically, assess the reliability of predictions, and convert complex data into meaningful scientific insight.For anyone seeking to understand how machine learning is reshaping pharmaceutical sciences, this book offers an authoritative and accessible starting point. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798906434210
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Introduction to Machine Learning in Pharmaceutical Sciences
Vendeur : California Books, Miami, FL, Etats-Unis
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Introduction to Machine Learning in Pharmaceutical Sciences (Hardcover)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. Pharmaceutical science is entering a new era in which data can reveal patterns, predict outcomes, and guide decisions that conventional analysis may overlook. Introduction to Machine Learning in Pharmaceutical Sciences provides a clear and practical pathway into this rapidly evolving field, connecting computational principles with real pharmaceutical challenges.Beginning with the foundations of artificial intelligence, machine learning, and data science, the book explains pharmaceutical datasets, data preparation, regression, classification, clustering, decision trees, and model evaluation in an accessible yet scientifically rigorous manner. Application-focused discussions demonstrate how these methods support drug discovery, formulation development, quality control, manufacturing, pharmacovigilance, and clinical decision-making.Designed for pharmacy students, teachers, researchers, and early-career pharmaceutical professionals, the book bridges the gap between theoretical algorithms and their responsible use in pharmaceutical research and healthcare. Readers gain the conceptual foundation needed to interpret models critically, assess the reliability of predictions, and convert complex data into meaningful scientific insight.For anyone seeking to understand how machine learning is reshaping pharmaceutical sciences, this book offers an authoritative and accessible starting point. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798906434210
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Introduction to Machine Learning in Pharmaceutical Sciences (Hardcover)
impression à la demandeVendeur : AussieBookSeller, Truganina, VIC, Australie
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. Pharmaceutical science is entering a new era in which data can reveal patterns, predict outcomes, and guide decisions that conventional analysis may overlook. Introduction to Machine Learning in Pharmaceutical Sciences provides a clear and practical pathway into this rapidly evolving field, connecting computational principles with real pharmaceutical challenges.Beginning with the foundations of artificial intelligence, machine learning, and data science, the book explains pharmaceutical datasets, data preparation, regression, classification, clustering, decision trees, and model evaluation in an accessible yet scientifically rigorous manner. Application-focused discussions demonstrate how these methods support drug discovery, formulation development, quality control, manufacturing, pharmacovigilance, and clinical decision-making.Designed for pharmacy students, teachers, researchers, and early-career pharmaceutical professionals, the book bridges the gap between theoretical algorithms and their responsible use in pharmaceutical research and healthcare. Readers gain the conceptual foundation needed to interpret models critically, assess the reliability of predictions, and convert complex data into meaningful scientific insight.For anyone seeking to understand how machine learning is reshaping pharmaceutical sciences, this book offers an authoritative and accessible starting point. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9798906434210
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