Statistics with R for Machine Learning

Harness the power of R to prepare and split data for machine learning models. 'Statistics with R for Machine Learning' is designed for data enthusiasts and professionals looking to enhance their data manipulation skills. Grounded in practical application, this book guides you through the intricacies of data structure, resampling approaches, and cross-validation methods, ensuring you master the essential steps for successful machine learning.

What You'll Learn

• Prepare data structures for effective machine learning
• Implement resampling techniques like V-Fold and Bootstrap
• Detect and handle missing or strange values in datasets
• Write and read data efficiently as CSV files
• Execute initial splits for robust model training

Who This Book Is For

This book is ideal for data analysts, statisticians, and machine learning practitioners who seek to deepen their understanding of data preparation. It assumes familiarity with basic statistical concepts and R programming.

What You Need to Know First

Readers should have a basic understanding of R programming and fundamental statistics. Prior exposure to data manipulation and cleaning techniques will be beneficial.

Why This Book

Unlike other textbooks, this book focuses on practical data preparation using real-world examples and resampling methods, offering a hands-on approach.

What's Inside

Authored by Mohsen Nady and published by Arcler Press, this 298-page volume includes comprehensive chapters on data preparation and advanced resampling techniques.