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[null,"1.","Record Nr.",null,"UNINA9910984669403321"]
[null,null,"Autore",null,"Wang Zhiyuan"]
[null,null,"Titolo",null,"Numerical Machine Learning"]
[null,null,"Pubbl/distr/stampa",null,"Sharjah : , : Bentham Science Publishers, , 2023"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"9789815136982"]
[{"$nulls":4},"9815136984"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (225 pages)"]
[null,null,"Soggetti",null,"Machine learning"]
[{"$nulls":4},"Numerical analysis"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover\n -- Title\n -- Copyright\n -- End User License Agreement\n -- Content\n -- Preface\n -- Introduction to Machine Learning\n -- Linear Regression  -- Regularization\n -- Logistic Regression\n -- Decision Tree\n -- Gradient Boosting\n -- Support Vector Machine\n -- K-means Clustering\n -- Subject Index"]
[null,null,null,"Sommario/riassunto","Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-"]
[{"$nulls":4},"rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features - Provides a concise introduction to numerical concepts in machine learning in simple terms - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables - Focuses on numerical examples while using small datasets for easy learning - Includes simple Python codes - Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses."]