Istituzioni di diritto privato / Adriano De Cupis

• Record Nr.: UNINA990005967540403321
• Autore: De Cupis, Adriano <1914- >
• Pubbl/distr/stampa: Milano : Giuffrè, 1983
• Edizione: [3. ed. agg.]
• Descrizione fisica: XX, 638 p. ; 22 cm
• Disciplina: 346
• Locazione: FGBC; DDCP
• Collocazione: VIII B 627; 19-L-180
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Il Collegio sindacale : attività di controllo e procedure pratiche / Alessandro Cotto..., [et al.]

• Record Nr.: UNISA990003015660203316
• Autore: COTTO, Alessandro
• Pubbl/distr/stampa: [Milanofiori, Assago] : IPSOA, 2007
• ISBN: 978-88-217-2602-6
• Descrizione fisica: 437 p. ; 24 cm
• Collana: Le Società ; 3
• Disciplina: 346.450664
• Soggetti: Società per azioni - Legislazione
• Collocazione: XXV.3.E 383 (IG II 1346)
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Disciplina dei contratti turistici e danno da vacanza rovinata / Ezio Guerinoni

• Record Nr.: UNISALENTO991000623079707536
• Autore: Guerinoni, Ezio
• Pubbl/distr/stampa: [Milanofiori, Assago] : IPSOA, 2009
• ISBN: 9788821730849
• Descrizione fisica: xiii, 289 p. ; 24 cm
• Collana: Le nuove frontiere della responsabilità civile ; 11
• Disciplina: 346.45024
• Soggetti: Contratto turistico
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Practical machine learning in R / / Fred Nwanganga, Mike Chapple

• Record Nr.: UNINA9910815757803321
• Autore: Nwanganga Fred Chukwuka
• Pubbl/distr/stampa: Indianapolis : , : John Wiley and Sons, , [2020]; ©2020
• ISBN: 1-5231-3319-8; 1-119-59157-0; 1-119-59153-8; 1-119-59154-6
• Descrizione fisica: 1 online resource (466 pages) : illustrations
• Disciplina: 617.9
• Soggetti: Machine learning; R (Computer program language); Aprenentatge automàtic; R (Llenguatge de programació); Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Sommario/riassunto: Guides 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.