Statistics Machine Learning Volume by Mohsen Nady (25 results)

Hardcover. Condition: Brand New. 185 pages. 9.00x6.00x9.02 inches. In Stock.

Hardcover. Condition: Brand New. 295 pages. 9.00x6.00x9.29 inches. In Stock.

Hardcover. Condition: Brand New. 397 pages. 5.98x0.79x9.02 inches. In Stock.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

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Condition: New. 2026. library. . . . . . Books ship from the US and Ireland.

Condition: New. 2025. library. . . . . . Books ship from the US and Ireland.

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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Before building predictive models, raw data must be cleaned, transformed, and prepared-a process known as data preprocessing. Effective preprocessing enhances the accuracy and reliability of machine learning algorithms. Statistics with R for Machine Learning: Data Preprocessing for Machine Learning using R provides an in-depth guide to statistical tools and techniques essential for preparing data. The book explains data normalization, missing value imputation, outlier detection, and feature engineering using R programming. It also introduces visualization tools and statistical validation methods. Practical examples and R scripts make it an ideal reference for students and data professionals. 397 pp. Englisch.…

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Resampling techniques are key to improving model performance and reliability in machine learning. This volume explores advanced resampling methods, including cross-validation, bootstrapping, and hyperparameter tuning, using R. Readers will learn how to apply these techniques to optimize model accuracy and prevent overfitting. Practical examples and case studies illustrate their real-world applications. This volume is an essential resource for data scientists and machine learning enthusiasts aiming to master resampling strategies. 185 pp. Englisch.…

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data preparation is the foundation of any successful machine learning project. This volume provides a comprehensive guide to cleaning, transforming, and splitting data for machine learning using R, including handling missing values, feature scaling, and stratified sampling. Practical examples and R code demonstrate how to optimize datasets for predictive modeling. The volume is essential for data scientists and machine learning practitioners seeking to build robust models. 295 pp. Englisch.…

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Before building predictive models, raw data must be cleaned, transformed, and prepared-a process known as data preprocessing. Effective preprocessing enhances the accuracy and reliability of machine learning algorithms. Statistics with R for Machine Learning: Data Preprocessing for Machine Learning using R provides an in-depth guide to statistical tools and techniques essential for preparing data. The book explains data normalization, missing value imputation, outlier detection, and feature engineering using R programming. It also introduces visualization tools and statistical validation methods. Practical examples and R scripts make it an ideal reference for students and data professionals.…

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data preparation is the foundation of any successful machine learning project. This volume provides a comprehensive guide to cleaning, transforming, and splitting data for machine learning using R, including handling missing values, feature scaling, and stratified sampling. Practical examples and R code demonstrate how to optimize datasets for predictive modeling. The volume is essential for data scientists and machine learning practitioners seeking to build robust models.…

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Resampling techniques are key to improving model performance and reliability in machine learning. This volume explores advanced resampling methods, including cross-validation, bootstrapping, and hyperparameter tuning, using R. Readers will learn how to apply these techniques to optimize model accuracy and prevent overfitting. Practical examples and case studies illustrate their real-world applications. This volume is an essential resource for data scientists and machine learning enthusiasts aiming to master resampling strategies.…