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{ "item_title" : "Mathematics for Machine Learning and AI", "item_author" : [" Randhir Singh Baghel "], "item_description" : "Mathematics for Machine Learning and AI provides a foundational and practical understanding of the core mathematical concepts that underpin modern artificial intelligence systems. It covers essential topics such as linear algebra, calculus, probability theory, statistics, optimization, and discrete mathematics, all tailored to their applications in machine learning and AI. This book bridges the gap between mathematical theory and practical implementation, making complex topics accessible through clear explanations, real-world examples, and hands-on problem-solving. Readers will learn how eigenvalues, gradients, probability distributions, and optimization algorithms drive intelligent systems-from neural networks and decision trees to deep learning and reinforcement learning. Designed for students, educators, and professionals, the book balances theoretical rigor with intuitive insights, offering both the mathematical depth and applied knowledge needed to excel in the evolving fields of data science, AI, and machine learning.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/6/20/844/647/6208446473_b.jpg", "price_data" : { "retail_price" : "71.00", "online_price" : "71.00", "our_price" : "71.00", "club_price" : "71.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Mathematics for Machine Learning and AI|Randhir Singh Baghel

Mathematics for Machine Learning and AI

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Overview

Mathematics for Machine Learning and AI provides a foundational and practical understanding of the core mathematical concepts that underpin modern artificial intelligence systems. It covers essential topics such as linear algebra, calculus, probability theory, statistics, optimization, and discrete mathematics, all tailored to their applications in machine learning and AI. This book bridges the gap between mathematical theory and practical implementation, making complex topics accessible through clear explanations, real-world examples, and hands-on problem-solving. Readers will learn how eigenvalues, gradients, probability distributions, and optimization algorithms drive intelligent systems-from neural networks and decision trees to deep learning and reinforcement learning. Designed for students, educators, and professionals, the book balances theoretical rigor with intuitive insights, offering both the mathematical depth and applied knowledge needed to excel in the evolving fields of data science, AI, and machine learning.

This item is Non-Returnable

Details

  • ISBN-13: 9786208446475
  • ISBN-10: 6208446473
  • Publisher: LAP Lambert Academic Publishing
  • Publish Date: June 2025
  • Dimensions: 9 x 6 x 0.25 inches
  • Shipping Weight: 0.33 pounds
  • Page Count: 104

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