Applied math data science di hossain (5 risultati)
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Da: Bluemindbooks, PACHECO, CA, U.S.A.Bluemindbooks
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Da: California Books, Miami, FL, U.S.A.California Books
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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
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Paperback. Condizione: new. Paperback. Applied Math for Data Science doesn't teach you everything-it teaches you what actually matters.If you've ever felt overwhelmed by dense math textbooks, endless theory, or courses that never connect to real-world work, this book is your shortcut.You don't need years of abstract mathematics…to succeed in data science, machine learning, or AI. What you need is a clear, practical understanding of the core ideas that show up every day on the job-and that's exactly what this book delivers. This book is designed to provide a practical, working understanding of the mathematics used in data science, machine learning, and AI. It focuses on the concepts and techniques most commonly applied in real-world work.It is not intended to be a comprehensive or rigorous treatment of mathematics. Formal proofs, advanced theoretical topics, and exhaustive derivations are intentionally minimized in favor of clarity, intuition, and application.Readers seeking a deep, formal study of mathematics may wish to supplement this book with traditional academic texts. The goal here is different: to help you understand, use, and reason about the math that actually matters in practice.What You'll LearnInside this book, you'll master the essential math behind modern data work-without getting lost in unnecessary theory: Linear Algebra - Vectors, matrices, PCA, and SVD explained with real-world intuitionProbability - Conditional probability, distributions, and Bayes theorem that actually stickStatistics - Hypothesis testing, confidence intervals, and avoiding common mistakesCalculus - Derivatives and gradients made simple for optimizationOptimization - Gradient descent and modern optimizers like Adam and RMSPropMachine Learning Math - Linear regression, logistic regression, neural networks, and attention Why This Book Is DifferentMost math books are written for mathematicians.This one is written for practitioners.Instead of long proofs and abstract theory, you get: Clear, plain-English explanationsStep-by-step worked examplesReal-world scenarios from industryProblem sets with full solutionsVisual intuition behind every conceptYou'll learn why the math matters, not just how to compute it.Who This Book Is ForAspiring data scientists and ML engineersStudents who want a practical math foundationProfessionals switching into AI or analyticsAnyone tired of "fluffy" explanations and overcomplicated theory What You'll GainBy the end of this book, you will: Understand the math behind machine learning modelsDebug models with confidenceMake better decisions using dataStop feeling intimidated by math The Bottom LineYou don't need to master everything.You need to master what matters.This book shows you exactly what that is. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
