Verwandte Artikel zu Regression Analysis with Classical and Statistical...
Regression Analysis with Classical and Statistical Learning Methods: An Easy Guide for Data Scientists, Business Analysts and Engineers using Python - Softcover
- Erscheinungsdatum2025
- ISBN 10 9348642510
- ISBN 13 9789348642516
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten502
Neu kaufen
Diesen Artikel anzeigen
EUR 60,79
Gratis für den Versand innerhalb von/der USA
Versandziele, Kosten & DauerSuchergebnisse für Regression Analysis with Classical and Statistical...
Beispielbild für diese ISBN
Regression Analysis with Classical and Statistical Learning Methods: An Easy Guide for Data Scientists, Business Analysts and Engineers using Python
Anbieter: California Books, Miami, FL, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers I-9789348642516
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Regression Analysis with Classical and Statistical Learning Methods (Paperback)
Anbieter: Grand Eagle Retail, Fairfield, OH, USA
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: new. Paperback. Regression is a powerful technique in data analysis for modeling relationships between variables, making it crucial for prediction, decision-making, and pattern recognition. This book offers an accessible introduction to regression modeling, tailored for postgraduate students in fields such as data science, engineering, statistics, mathematics, business, and the sciences. It simplifies complex mathematical concepts and emphasizes real-world applications, complemented by coding examples to reinforce key concepts.The book covers classical regression methods including simple and multiple linear regression, polynomial regression, and logistic regression. It also addresses regression diagnostics, such as model evaluation, outlier detection, and assessment of model assumptions. By integrating classical methods with modern machine learning techniques, it offers a unique perspective. Machine learning techniques like support vector regression, decision trees, and artificial neural networks (ANN) for regression tasks are introduced, demonstrating their complementarity to classical methods through practical examples. The book also explores advanced methods such as Ridge, Lasso, Elastic Net, Principal Component Regression, and Generalized Linear Models (GLMs). These techniques are demonstrated using Python libraries like Statsmodels and Scikit-learn, enabling students to engage in practical learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9789348642516
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Regression Analysis with Classical and Statistical Learning Methods: An Easy Guide for Data Scientists, Business Analysts and Engineers using Python
ISBN 10: 9348642510
ISBN 13: 9789348642516
Neu
Softcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. In. Bestandsnummer des Verkäufers ria9789348642516_new
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Regression Analysis with Classical and Statistical Learning Methods
Print-on-DemandAnbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 502 pp. Englisch. Bestandsnummer des Verkäufers 9789348642516
Anzahl: 2 verfügbar
Beispielbild für diese ISBN
Regression Analysis with Classical and Statistical Learning Methods (Paperback)
Anbieter: CitiRetail, Stevenage, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: new. Paperback. Regression is a powerful technique in data analysis for modeling relationships between variables, making it crucial for prediction, decision-making, and pattern recognition. This book offers an accessible introduction to regression modeling, tailored for postgraduate students in fields such as data science, engineering, statistics, mathematics, business, and the sciences. It simplifies complex mathematical concepts and emphasizes real-world applications, complemented by coding examples to reinforce key concepts.The book covers classical regression methods including simple and multiple linear regression, polynomial regression, and logistic regression. It also addresses regression diagnostics, such as model evaluation, outlier detection, and assessment of model assumptions. By integrating classical methods with modern machine learning techniques, it offers a unique perspective. Machine learning techniques like support vector regression, decision trees, and artificial neural networks (ANN) for regression tasks are introduced, demonstrating their complementarity to classical methods through practical examples. The book also explores advanced methods such as Ridge, Lasso, Elastic Net, Principal Component Regression, and Generalized Linear Models (GLMs). These techniques are demonstrated using Python libraries like Statsmodels and Scikit-learn, enabling students to engage in practical learning. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9789348642516
Anzahl: 1 verfügbar
Foto des Verkäufers
Regression Analysis with Classical and Statistical Learning Methods : An Easy Guide for Data Scientists, Business Analysts and Engineers using Python
Print-on-DemandAnbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Regression is a powerful technique in data analysis for modeling relationships between variables, making it crucial for prediction, decision-making, and pattern recognition. This book offers an accessible introduction to regression modeling, tailored for postgraduate students in fields such as data science, engineering, statistics, mathematics, business, and the sciences. It simplifies complex mathematical concepts and emphasizes real-world applications, complemented by coding examples to reinforce key concepts.The book covers classical regression methods including simple and multiple linear regression, polynomial regression, and logistic regression. It also addresses regression diagnostics, such as model evaluation, outlier detection, and assessment of model assumptions. By integrating classical methods with modern machine learning techniques, it offers a unique perspective. Machine learning techniques like support vector regression, decision trees, and artificial neural networks (ANN) for regression tasks are introduced, demonstrating their complementarity to classical methods through practical examples. The book also explores advanced methods such as Ridge, Lasso, Elastic Net, Principal Component Regression, and Generalized Linear Models (GLMs). These techniques are demonstrated using Python libraries like Statsmodels and Scikit-learn, enabling students to engage in practical learning. Bestandsnummer des Verkäufers 9789348642516
Anzahl: 2 verfügbar