Andere Kunden interessierten sich auch für
![Data Science and Machine Learning Demystified]( "Data Science and Machine Learning Demystified")
Data Science and Machine Learning Demystified17,99 €![Machine Learning for Societal Improvement, Modernization, and Progress]( "Machine Learning for Societal Improvement, Modernization, and Progress")
Machine Learning for Societal Improvement, Modernization, and Progress200,99 €![Practical Predictive Analytics]( "Practical Predictive Analytics")
Practical Predictive Analytics57,99 €![Mastering Predictive Analytics with scikit-learn and TensorFlow]( "Mastering Predictive Analytics with scikit-learn and TensorFlow")
Mastering Predictive Analytics with scikit-learn and TensorFlow33,99 €![Mastering Predictive Analytics with R, Second Edition]( "Mastering Predictive Analytics with R, Second Edition")
Mastering Predictive Analytics with R, Second Edition56,99 €![Learning Predictive Analytics with Python]( "Learning Predictive Analytics with Python")
Learning Predictive Analytics with Python55,99 €![Predictive Analytics and Data Optimization]( "Predictive Analytics and Data Optimization")
Predictive Analytics and Data Optimization156,99 €
This book was written by the architect of two MS Analytics programs and one undergraduate specialization in Business Analytics, with over a decade of experience teaching and practicing predictive analytics, and co-chairing premier academic conference mini-track in this field. The author's goal is to provide strong but understandable conceptual foundations and practical material for graduate students and managers, describing how to frame a business question, identify various model specification (i.e., feature engineering) and model methods (explainable and black box), select the optimal model based on the bias, variance, and cross-validation testing, and interpret results with meaningful storytelling for clients and managers. The book contains two components: (1) the main text with two sections-one with conceptual, mathematical, and managerial foundations, the other about advanced predictive modeling methods based on machine learning. The main text is further subdivided into two sections-Section 1 contains basic fundamentals of statistics and predictive modeling; Section 2 provides a deeper discussion of machine learning and advance predictive modeling approaches based on machine learning and cross-validation methods; and (2) a free appendix companion with annotated R Markdown code with hands-on applications, posted in GitHub.