LEADER 03527nam 22004573 450 001 9911008938103321 005 20240107090245.0 010 $a9789391246860 010 $a9391246869 035 $a(MiAaPQ)EBC31054978 035 $a(Au-PeEL)EBL31054978 035 $a(Exl-AI)31054978 035 $a(CKB)29516037900041 035 $a(OCoLC)1417195183 035 $a(EXLCZ)9929516037900041 100 $a20240107d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aUltimate Parallel and Distributed Computing with Julia for Data Science 205 $a1st ed. 210 1$aDelhi :$cOrange Education PVT Ltd,$d2024. 210 4$dİ2023. 215 $a1 online resource (272 pages) 311 08$aPrint version: Dash, Nabanita Ultimate Parallel and Distributed Computing with Julia for Data Science: Excel in Data Analysis, Statistical Modeling and Machine Learning by Leveraging MLBase. jl and MLJ. jl to Optimize Workflows Delhi : Orange Education PVT Ltd,c2024 327 $aCover Page -- Title Page -- Copyright Page -- Dedication Page -- About the Author -- About the Technical Reviewers -- Acknowledgements -- Preface -- Errata -- Table of Contents -- 1. Julia In Data Science Arena -- Introduction -- Structure -- Introducing Data Science -- Defining Data Science -- The need for Task Automation -- Introducing Statistics -- Introducing machine learning -- Drawing Correlations from Raw Data -- Explaining the Need for Data Analysis -- Introducing Julia -- Astounding Julia language!! -- Julia: Ideal for Data Analysis -- Limitations of Julia -- Conclusion -- Points to Remember -- References -- 2. Getting Started with Julia -- Introduction -- Structure$7Generated by AI. 330 $aThis book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results. The handbook culminates in optimizing workflows with Julia's parallel and distributed computing capabilities, ensuring efficient and scalable data processing using Distributions.jl, Distributed.jl and SharedArrays.jl. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning. 606 $aJulia (Computer program language)$7Generated by AI 606 $aMachine learning$7Generated by AI 615 0$aJulia (Computer program language) 615 0$aMachine learning 700 $aDash$b Nabanita$01826083 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911008938103321 996 $aUltimate Parallel and Distributed Computing with Julia for Data Science$94394008 997 $aUNINA