LEADER 04006nam 2200493 450 001 9910796534103321 005 20200520144314.0 035 $a(CKB)4100000000880843 035 $a(Safari)9781783551606 035 $a(OCoLC)1006894433 035 $a(WaSeSS)IndRDA00088515 035 $a(Au-PeEL)EBL5058274 035 $a(CaPaEBR)ebr11446111 035 $a(OCoLC)1005006117 035 $a(CaSebORM)9781783551606 035 $a(MiAaPQ)EBC5058274 035 $a(PPN)228048214 035 $a(EXLCZ)994100000000880843 100 $a20171018h20172017 uy 0 101 0 $aeng 135 $aurunu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aApache spark 2.x machine learning cookbook $eover 100 recipes to simplify machine learning model implementations with Spark /$fSiamak Amirghodsi [and three others] 205 $a1st edition 210 1$aBirmingham, England :$cPackt Publishing,$d2017. 210 4$dİ2017 215 $a1 online resource (1 volume) $cillustrations 300 $aIncludes index. 311 $a1-78355-160-7 311 $a1-78217-460-5 330 $aSimplify machine learning model implementations with Spark About This Book Solve the day-to-day problems of data science with Spark This unique cookbook consists of exciting and intuitive numerical recipes Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data Who This Book Is For This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem. What You Will Learn Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark Build a recommendation engine that scales with Spark Find out how to build unsupervised clustering systems to classify data in Spark Build machine learning systems with the Decision Tree and Ensemble models in Spark Deal with the curse of high-dimensionality in big data using Spark Implement Text analytics for Search Engines in Spark Streaming Machine Learning System implementation using Spark In Detail Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems. Style and approach This book is packed with intu... 606 $aData mining$xComputer programs 615 0$aData mining$xComputer programs. 676 $a006.754 702 $aAmirghodsi$b Siamak 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910796534103321 996 $aApache spark 2.x machine learning cookbook$93835984 997 $aUNINA