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Auteur Jesus SALCEDO |
Documents disponibles écrits par cet auteur (2)
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Titre : SPSS Statistics For Dummies Ed. 4 Type de document : e-book Auteurs : Jesus SALCEDO Editeur : For Dummies Année de publication : 2020 ISBN/ISSN/EAN : 9781119560838 Note générale : copyrighted Langues : Anglais (eng) Résumé : The fun and friendly guide to mastering IBM's Statistical Package for the Social Sciences Written by an author team with a combined 55 years of experience using SPSS, this updated guide takes the guesswork out of the subject and helps you get the most out of using the leader in predictive analysis. Covering the latest release and updates to SPSS 27.0, and including more than 150 pages of basic statistical theory, it helps you understand the mechanics behind the calculations, perform predictive analysis, produce informative graphs, and more. You'll even dabble in programming as you expand SPSS functionality to suit your specific needs. Master the fundamental mechanics of SPSS Learn how to get data into and out of the program Graph and analyze your data more accurately and efficiently Program SPSS with Command Syntax Get ready to start handling data like a pro?with step-by-step instruction and expert advice! Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88945253 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=578837
Titre : Machine Learning for Data Mining Type de document : e-book Auteurs : Jesus SALCEDO Editeur : PACKT PUBLISHING Année de publication : 2019 ISBN/ISSN/EAN : 9781838828974 Note générale : copyrighted Langues : Anglais (eng) Résumé : Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key Features Learn how to apply machine learning techniques in the field of data science Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results A step-by-step approach to improving model development and performance Book Description Machine learning (ML) combined with data mining can give you amazing results in your data mining work by empowering you with several ways to look at data. This book will help you improve your data mining techniques by using smart modeling techniques. This book will teach you how to implement ML algorithms and techniques in your data mining work. It will enable you to pair the best algorithms with the right tools and processes. You will learn how to identify patterns and make predictions with minimal human intervention. You will build different types of ML models, such as the neural network, the Support Vector Machines (SVMs), and the Decision tree. You will see how all of these models works and what kind of data in the dataset they are suited for. You will learn how to combine the results of different models in order to improve accuracy. Topics such as removing noise and handling errors will give you an added edge in model building and optimization. By the end of this book, you will be able to build predictive models and extract information of interest from the dataset What you will learn Hone your model-building skills and create the most accurate models Understand how predictive machine learning models work Prepare your data to acquire the best possible results Combine models in order to suit the requirements of different types of data Analyze single and multiple models and understand their combined results Derive worthwhile insights from your data using histograms and graphs Who this book is for If you are a data scientist, data analyst, and data mining professional and are keen to achieve a 30% higher salary by adding machine learning to your skillset, then this is the ideal book for you. You will learn to apply machine learning techniques to various data mining challenges. No prior knowledge of machine learning is assumed. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88869967 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=485402
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