{ "item_title" : "Analytical Geometry for Machine Learning", "item_author" : [" Alice Schwartz", "Hayden Van Der Post", "Leonard K. Sato "], "item_description" : "Reactive PublishingAnalytical geometry forms the mathematical foundation behind modern machine learning systems, enabling models to interpret structure, distance, and transformation in high-dimensional space.This book presents a structured approach to analytical geometry with a focus on its role in machine learning and applied AI. It develops the core concepts required to understand how vectors, coordinate systems, and geometric transformations operate within data-driven models.Topics include vector operations, linear transformations, coordinate mappings, and spatial representations used in machine learning workflows. Each concept is explored with practical context, connecting geometric intuition to real-world applications such as feature spaces, embeddings, and model optimization.Designed for readers with a basic background in mathematics, this book bridges the gap between classical geometry and modern computational systems. It provides a clear framework for understanding how spatial reasoning underpins many of the techniques used in machine learning today.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/825/251/9798252518213_b.jpg", "price_data" : { "retail_price" : "43.99", "online_price" : "43.99", "our_price" : "43.99", "club_price" : "43.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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