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Synopsis
Unlock the power of machine learning in R with R for Data Science: Implementing Machine Learning Models. This comprehensive guide equips data scientists, analysts, and R enthusiasts with the practical skills needed to build, evaluate, and deploy advanced machine learning solutions across domains. Covering both fundamental and advanced topics, this book blends theory, hands-on examples, and real-world workflows to empower readers to harness R’s full capabilities.Learn how to:• Preprocess, clean, and transform data for robust analysis.• Build predictive models with regression, classification, and time series techniques.• Apply natural language processing and text analytics to extract insights from unstructured data.• Explore clustering, dimensionality reduction, and anomaly detection in unsupervised learning.• Optimize models through hyperparameter tuning, ensemble methods, and stacking strategies.• Develop reproducible workflows, pipelines, and deployment-ready solutions in R.
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R for Data Science
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LAP LAMBERT Academic Publishing, 2026
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ISBN 13: 9786209540547
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R for Data Science
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LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209540546
ISBN 13: 9786209540547
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R for Data Science: Implementing Machine Learning Models
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LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209540546
ISBN 13: 9786209540547
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R for Data Science: Implementing Machine Learning Models
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Omniscriptum, LAP Lambert Academic Publishing, 2026
ISBN 10: 6209540546
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Unlock the power of machine learning in R with R for Data Science: Implementing Machine Learning Models. This comprehensive guide equips data scientists, analysts, and R enthusiasts with the practical skills needed to build, evaluate, and deploy advanced machine learning solutions across domains. Covering both fundamental and advanced topics, this book blends theory, hands-on examples, and real-world workflows to empower readers to harness R's full capabilities.Learn how to:- Preprocess, clean, and transform data for robust analysis.- Build predictive models with regression, classification, and time series techniques.- Apply natural language processing and text analytics to extract insights from unstructured data.- Explore clustering, dimensionality reduction, and anomaly detection in unsupervised learning.- Optimize models through hyperparameter tuning, ensemble methods, and stacking strategies.- Develop reproducible workflows, pipelines, and deployment-ready solutions in R. 164 pp. Englisch. Seller Inventory # 9786209540547
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R for Data Science: Implementing Machine Learning Models
Published by
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209540546
ISBN 13: 9786209540547
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Unlock the power of machine learning in R with R for Data Science: Implementing Machine Learning Models. This comprehensive guide equips data scientists, analysts, and R enthusiasts with the practical skills needed to build, evaluate, and deploy advanced machine learning solutions across domains. Covering both fundamental and advanced topics, this book blends theory, hands-on examples, and real-world workflows to empower readers to harness R's full capabilities.Learn how to:- Preprocess, clean, and transform data for robust analysis.- Build predictive models with regression, classification, and time series techniques.- Apply natural language processing and text analytics to extract insights from unstructured data.- Explore clustering, dimensionality reduction, and anomaly detection in unsupervised learning.- Optimize models through hyperparameter tuning, ensemble methods, and stacking strategies.- Develop reproducible workflows, pipelines, and deployment-ready solutions in R. Seller Inventory # 9786209540547
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R for Data Science (Paperback)
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Paperback. Condition: new. Paperback. Unlock the power of machine learning in R with R for Data Science: Implementing Machine Learning Models. This comprehensive guide equips data scientists, analysts, and R enthusiasts with the practical skills needed to build, evaluate, and deploy advanced machine learning solutions across domains. Covering both fundamental and advanced topics, this book blends theory, hands-on examples, and real-world workflows to empower readers to harness R's full capabilities.Learn how to: - Preprocess, clean, and transform data for robust analysis.- Build predictive models with regression, classification, and time series techniques.- Apply natural language processing and text analytics to extract insights from unstructured data.- Explore clustering, dimensionality reduction, and anomaly detection in unsupervised learning.- Optimize models through hyperparameter tuning, ensemble methods, and stacking strategies.- Develop reproducible workflows, pipelines, and deployment-ready solutions in R. 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 # 9786209540547
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R for Data Science: Implementing Machine Learning Models
Published by
LAP LAMBERT Academic Publishing Feb 2026, 2026
ISBN 10: 6209540546
ISBN 13: 9786209540547
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Unlock the power of machine learning in R with R for Data Science: Implementing Machine Learning Models. This comprehensive guide equips data scientists, analysts, and R enthusiasts with the practical skills needed to build, evaluate, and deploy advanced machine learning solutions across domains. Covering both fundamental and advanced topics, this book blends theory, hands-on examples, and real-world workflows to empower readers to harness R's full capabilities.Learn how to:¿ Preprocess, clean, and transform data for robust analysis.¿ Build predictive models with regression, classification, and time series techniques.¿ Apply natural language processing and text analytics to extract insights from unstructured data.¿ Explore clustering, dimensionality reduction, and anomaly detection in unsupervised learning.¿ Optimize models through hyperparameter tuning, ensemble methods, and stacking strategies.¿ Develop reproducible workflows, pipelines, and deployment-ready solutions in R.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 164 pp. Englisch. Seller Inventory # 9786209540547