{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":29,"n_cols":5,"rows_kept":29,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[11,2,11,3],[11,4,11,5],[12,2,12,3],[12,4,12,5],[13,2,13,3],[13,4,13,5],[14,2,14,3],[15,2,15,3],[17,2,17,3],[18,2,18,3],[19,2,19,3],[20,2,20,3],[23,2,23,3],[24,2,24,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910306957003321"]
[null,null,"Titolo",null,"Il Museo di arte sacra a Certaldo / a cura di Rosanna Caterina Proto Pisani ; presentazione di Antonio Paolucci"]
[null,null,"Pubbl/distr/stampa",null,"[S. l.] : Becocci/Scala, [200?]"]
[null,null,"Descrizione fisica",null,"79 p. : ill. ; 24 cm"]
[null,null,"Collana",null,"Biblioteca de \"Lo studiolo\" ; 26"]
[null,null,"Disciplina",null,"704.9482074"]
[null,null,"Locazione",null,"FLFBC"]
[null,null,"Collocazione",null,"704.948 CATALOGHI 12"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910790503303321"]
[null,null,"Autore",null,"Lantz Brett"]
[null,null,"Titolo",null,"Machine learning with R / / Brett Lantz"]
[null,null,"Pubbl/distr/stampa",null,"Birmingham : , : Packt Publishing, , 2013"]
[null,null,"ISBN",null,"1-68015-358-7"]
[{"$nulls":4},"1-78216-215-1"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (396 p.)"]
[null,null,"Collana",null,"Community experience distilled"]
[null,null,"Soggetti",null,"Machine learning - Statistical methods"]
[{"$nulls":4},"R (Computer program language)"]
[{"$nulls":4},"Programming languages (Electronic computers)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,null,"Sommario/riassunto","R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning. Harness the power of R for statistical computing and data science Use R to apply common machine learning algorithms with real-world applications Prepare, examine, and visualize data for analysis Understand how to choose between machine learning models Packed with clear instructions to explore, forecast, and classify data In Detail Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of \"big data\" and \"data science\". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. \"Machine Learning with R\" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data "]
[{"$nulls":4},"for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. \"Machine Learning with R\" will provide you with the analytical tools you need to quickly gain insight from complex data."]