This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own.
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Prof Lim Kian Guan (PhD, MS, MA Stanford University (1981–1986). BSc I, UMIST (1975–1978)) is Professor Emeritus at Singapore Management University (2001–2024). Previously he was a Professor at National University of Singapore (1980–2001). Prof Lim has published several books with World Scientific and de Gruyter Berlin. He was an editorial board member of International Journal of Data Science and Analytics with Springer.
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Hardcover. Condition: new. Hardcover. This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naive Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9789819811236
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Hardcover. Condition: new. Hardcover. This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naive Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9789819811236
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