LEADER 00860nam0-22003251i-450- 001 990005967540403321 005 20120514190746.0 035 $a000596754 035 $aFED01000596754 035 $a(Aleph)000596754FED01 035 $a000596754 100 $a20120514d1983----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $a--------00-yy 200 1 $aIstituzioni di diritto privato$fAdriano De Cupis 205 $a3. ed. agg. 210 $aMilano$cGiuffrč$d1983 215 $aXX, 638 p.$d22 cm 676 $a346 700 1$aDe Cupis,$bAdriano$f<1914- >$0226819 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005967540403321 952 $aVIII B 627$b116921$fFGBC 952 $a19-L-180$b4000$fDDCP 959 $aFGBC 959 $aDDCP 996 $aIstituzioni di diritto privato$9196680 997 $aUNINA LEADER 01168nam--2200385---450- 001 990003015660203316 005 20090728142240.0 010 $a978-88-217-2602-6 035 $a000301566 035 $aUSA01000301566 035 $a(ALEPH)000301566USA01 035 $a000301566 100 $a20071119d2007----km-y0itay50------ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $a<> Collegio sindacale$eattivitā di controllo e procedure pratiche$fAlessandro Cotto..., [et al.] 210 $a[Milanofiori, Assago]$cIPSOA$d2007 215 $a437 p.$d24 cm 225 2 $a<> Societā$v3 410 0$12001$a<> Societā$v3 454 1$12001 461 1$1001-------$12001 606 0 $aSocietā per azioni$xLegislazione 676 $a346.450664 700 1$aCOTTO,$bAlessandro$0414228 801 0$aIT$bsalbc$gISBD 912 $a990003015660203316 951 $aXXV.3.E 383 (IG II 1346)$b15944 EC$cXXV.3.E 383 (IG II)$d00063469 959 $aBK 969 $aGIU 979 $aFIORELLA$b90$c20071119$lUSA01$h0915 979 $aRSIAV1$b90$c20090728$lUSA01$h1422 996 $aCollegio sindacale$91024927 997 $aUNISA LEADER 01074nam a2200289 i 4500 001 991001996589707536 005 20020507155005.0 008 000218s1989 it ||| | ita 020 $a8813166176 035 $ab11594196-39ule_inst 035 $aLE02728748$9ExL 040 $aDip.to Studi Giuridici$bita 082 0 $a345.4502 084 $aPEN-X/C 100 1 $aCanestrari, Stefano$0437934 245 12$aL'illecito penale preterintenzionale /$cStefano Canestrari 260 $aPadova :$bCEDAM,$c1989 300 $a305 p. ;$c24 cm. 490 0 $aCollana di studi penalistici. N.S. ;$v5 650 4$aReato preterintenzionale 907 $a.b11594196$b01-03-17$c02-07-02 912 $a991001996589707536 945 $aLE027 PEN-X/C 9$g2$i2027000281169$lle027$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i11804865$z02-07-02 945 $aLE027 PEN-X/C 10$g1$i2027000281152$lle027$o-$pE0.00$q-$rl$s- $t0$u1$v0$w1$x0$y.i11804877$z02-07-02 996 $aIllecito penale preterintenzionale$9889430 997 $aUNISALENTO 998 $ale027$b01-01-00$cm$da $e-$fita$git $h2$i2 LEADER 00968nam a2200241 i 4500 001 991000623079707536 008 100304s2009 it 000 0 ita d 020 $a9788821730849 035 $ab13885820-39ule_inst 040 $aDip.to Studi Giuridici$bita 082 0 $a346.45024 100 1 $aGuerinoni, Ezio $0280852 245 10$aDisciplina dei contratti turistici e danno da vacanza rovinata /$cEzio Guerinoni 260 $a[Milanofiori, Assago] :$bIPSOA,$c2009 300 $axiii, 289 p. ;$c24 cm 440 3$aLe nuove frontiere della responsabilitā civile ;$v11 650 4$aContratto turistico 907 $a.b13885820$b02-04-14$c04-03-10 912 $a991000623079707536 945 $aLE027 346.02 GUE01.01$g1$i2027000238521$lle027$o-$pE34.00$q-$rl$s- $t0$u10$v2$w10$x0$y.i1510011x$z29-03-10 996 $aDisciplina dei contratti turistici e danno da vacanza rovinata$9226153 997 $aUNISALENTO 998 $ale027$b04-03-10$cm$da $e-$fita$git $h0$i0 LEADER 03571nam 2200553 450 001 9910815757803321 005 20220422110803.0 010 $a1-5231-3319-8 010 $a1-119-59157-0 010 $a1-119-59153-8 010 $a1-119-59154-6 035 $a(CKB)4100000010870980 035 $a(MiAaPQ)EBC6174019 035 $a(PPN)270072101 035 $a(OCoLC)1151188553 035 $a(CaSebORM)9781119591511 035 $a(EXLCZ)994100000010870980 100 $a20200730h20202020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPractical machine learning in R /$fFred Nwanganga, Mike Chapple 210 1$aIndianapolis :$cJohn Wiley and Sons,$d[2020] 210 4$aŠ2020 215 $a1 online resource (466 pages) $cillustrations 300 $aIncludes index. 311 $a1-119-59151-1 330 $aGuides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning?a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions?allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field. 606 $aMachine learning 606 $aR (Computer program language) 606 $aAprenentatge automātic$2thub 606 $aR (Llenguatge de programaciķ)$2thub 608 $aLlibres electrōnics$2thub 615 0$aMachine learning. 615 0$aR (Computer program language) 615 7$aAprenentatge automātic 615 7$aR (Llenguatge de programaciķ) 676 $a617.9 700 $aNwanganga$b Fred Chukwuka$01704176 702 $aChapple$b Mike 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910815757803321 996 $aPractical machine learning in R$94089961 997 $aUNINA