{"product_id":"big-data-mathematics-and-ai-algorithms-foundations-of-scalable-machine-learning-9798262954834","title":"Big data mathematics and ai algorithms: Foundations of scalable machine learning","description":"\u003cp\u003e • Author(s): Anshuman Mishra\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Databases - Data Mining\u003c\/p\u003e\u003cp\u003eThe 21st century has been defined by data - massive, continuous, and omnipresent. Every human action, every digital interaction, every business transaction generates data. This overwhelming flood of data, commonly referred to as \u003cb\u003eBig Data\u003c\/b\u003e, has transformed how we perceive the world, how businesses operate, and how decisions are made. However, \u003cb\u003eBig Data alone is meaningless without the mathematical tools, scalable algorithms, and computational frameworks that allow us to transform raw data into actionable intelligence.\u003c\/b\u003e\u003cbr\u003eThis book, \u003cb\u003e\"Big Data Mathematics and AI Algorithms: Foundations of Scalable Machine Learning,\"\u003c\/b\u003e has been written to bridge a critical gap in the understanding of the mathematical foundations of Big Data and their application in Artificial Intelligence (AI). While countless books exist on AI, machine learning, and data science, very few focus specifically on the mathematical rigor required to handle \u003cb\u003elarge-scale datasets\u003c\/b\u003e and \u003cb\u003eparallel computation\u003c\/b\u003e.\u003cbr\u003eThe main purpose of this book is to \u003cb\u003eequip students, researchers, and professionals\u003c\/b\u003e with both \u003cb\u003ethe theoretical understanding\u003c\/b\u003e and \u003cb\u003epractical insights\u003c\/b\u003e needed to design, implement, and scale AI algorithms for Big Data applications. It does not simply teach machine learning from a traditional perspective; it shows \u003cb\u003ehow mathematics makes large-scale AI possible\u003c\/b\u003e, how \u003cb\u003eparallel computations\u003c\/b\u003e make it efficient, and how \u003cb\u003escalable algorithms\u003c\/b\u003e make it relevant in real-world scenarios. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhy This Book is Important\u003c\/b\u003e\u003cbr\u003eThe world is moving toward \u003cb\u003edata-driven intelligence\u003c\/b\u003e at an unprecedented scale. From healthcare systems processing petabytes of patient records, to e-commerce platforms recommending products to billions of users, to self-driving cars processing terabytes of sensor data per hour - \u003cb\u003emathematics is the silent engine\u003c\/b\u003e that powers every computation.\u003cbr\u003eThis book is important because it: \u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eBrings Mathematical Clarity to Big Data and AI: \u003c\/b\u003e\u003cbr\u003eStudents often learn AI algorithms as \"recipes\" - plug in data, run code, get results - but fail to understand the mathematical reasoning behind why an algorithm works, how it converges, and how it scales. This book explains the \u003cb\u003elinear algebra, probability, and optimization foundations\u003c\/b\u003e that power scalable AI models.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eFocuses on Scalability: \u003c\/b\u003e\u003cbr\u003eTraditional machine learning works fine on small datasets, but \u003cb\u003eBig Data demands scalable solutions\u003c\/b\u003e. This book explains distributed versions of common algorithms, teaches readers how to parallelize computations, and introduces tools like \u003cb\u003eMapReduce, Spark, and GPU-based training\u003c\/b\u003e.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eBuilds Practical Competence: \u003c\/b\u003e\u003cbr\u003eEvery chapter connects theory to practice with examples, case studies, and computational strategies. This helps readers move from understanding equations to implementing them efficiently.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePrepares for Research and Industry: \u003c\/b\u003e\u003cbr\u003eThe book is particularly valuable for \u003cb\u003eUGC NET aspirants, graduate students, data scientists, and AI engineers\u003c\/b\u003e, as it combines deep theory with industry-level frameworks.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003c\/p\u003e\u003cb\u003eHow This Book is Structured\u003c\/b\u003e\u003cbr\u003eThis book is divided into \u003cb\u003eten carefully crafted chapters\u003c\/b\u003e, each designed to progressively develop the reader's understanding.\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eChapter 1\u003c\/b\u003e introduces the landscape of Big Data and AI, explaining the need for scalable algorithms.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eChapter 2\u003c\/b\u003e builds the \u003cb\u003elinear algebra foundation\u003c\/b\u003e, which is essential for matrix operations, dimensionality reduction, and data transformations.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eChapter 3\u003c\/b\u003e covers \u003cb\u003eprobability, statistics, and stochastic processes\u003c\/b\u003e, which are at the heart of AI models.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eChapter 4\u003c\/b\u003e teaches \u003cb\u003eoptimization techniques\u003c\/b\u003e that enable model training, including distributed optimization for large datasets.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eChapter 5\u003c\/b\u003e explains the \u003cb\u003eparallel and distributed computing fundamentals\u003c\/b\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Atlantic Books","offers":[{"title":"Paperback","offer_id":46333249355927,"sku":"9798262954834","price":1551.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798262954834.webp?v=1768669048","url":"https:\/\/atlanticbooks.com\/products\/big-data-mathematics-and-ai-algorithms-foundations-of-scalable-machine-learning-9798262954834","provider":"Atlantic Books","version":"1.0","type":"link"}