LEADER 00952nam a22002291i 4500 001 991004215769707536 005 20020828140030.0 008 020828s1973 it |||||||||||||||||ita 035 $ab11942460-39ule_inst 035 $aARCHE-003227$9ExL 040 $aDip.to Filologia Ling. e Lett.$bita$cA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l. 100 1 $aCarducci, Giosuč$0439461 245 10$aCacce in rima dei secoli 14. e 15. /$cGiosue Carducci 260 $aBologna :$bA.M.I.S.,$c1973 300 $a128 p. ;$c24 cm 490 0$aBiblioteca storico giuridica e artistico letteraria.$pLetteratura musica teatro ;$v3 907 $a.b11942460$b28-04-17$c01-04-03 912 $a991004215769707536 945 $aLE008 FL.M. (f.r.) XXII A 150$g1$i2008000507756$lle008$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i12217177$z01-04-03 996 $aCacce in rima dei secoli 14. e 15$9913678 997 $aUNISALENTO 998 $ale008$b01-04-03$cm$da $e-$fita$git $h0$i1 LEADER 04372nam 22005895 450 001 9910678248403321 005 20250702133225.0 010 $a9783031023637$b(electronic bk.) 010 $z9783031023620 024 7 $a10.1007/978-3-031-02363-7 035 $a(MiAaPQ)EBC7206999 035 $a(Au-PeEL)EBL7206999 035 $a(CKB)26183503200041 035 $a(DE-He213)978-3-031-02363-7 035 $a(PPN)269096442 035 $a(EXLCZ)9926183503200041 100 $a20230301d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aThinking Data Science $eA Data Science Practitioner?s Guide /$fby Poornachandra Sarang 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (366 pages) $cillustrations 225 1 $aThe Springer Series in Applied Machine Learning,$x2520-1301 311 08$aPrint version: Sarang, Poornachandra Thinking Data Science Cham : Springer International Publishing AG,c2023 9783031023620 327 $aChapter. 1. Data Science Process -- Chapter. 2. Dimensionality Reduction - Creating Manageable Training Datasets -- Chapter. 3. Classical Algorithms - Over-view -- Chapter. 4. Regression Analysis -- Chapter. 5. Decision Tree -- Chapter. 6. Ensemble - Bagging and Boosting -- Chapter. 7. K-Nearest Neighbors -- Chapter. 8. Naive Bayes -- Chapter. 9. Support Vector Machines: A supervised learning algorithm for Classification and Regression -- Chapter. 10. Clustering Overview -- Chapter. 11. Centroid-based Clustering -- Chapter. 12. Connectivity-based Clustering -- Chapter. 13. Gaussian Mixture Model -- Chapter. 14. Density-based -- Chapter. 15 -- BIRCH -- Chapter. 16. CLARANS -- Chapter. 17. Affinity Propagation Clustering -- Chapter. 18. STING -- Chapter. 19. CLIQUE -- Chapter. 20. Artificial Neural Networks -- Chapter. 21. ANN-based Applications -- Chapter. 22. Automated Tools -- Chapter. 23. DataScientist?s Ultimate Workflow. 330 $aThis definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single ?Cheat Sheet?. The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big. 410 0$aThe Springer Series in Applied Machine Learning,$x2520-1301 606 $aMachine learning 606 $aArtificial intelligence$xData processing 606 $aArtificial intelligence 606 $aMachine Learning 606 $aData Science 606 $aArtificial Intelligence 615 0$aMachine learning. 615 0$aArtificial intelligence$xData processing. 615 0$aArtificial intelligence. 615 14$aMachine Learning. 615 24$aData Science. 615 24$aArtificial Intelligence. 676 $a006.31 676 $a005.7 700 $aSarang$b P. G$g(Poornachandra G.),$0476229 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910678248403321 996 $aThinking Data Science$93071644 997 $aUNINA