Practical statistical learning data (46 résultats)

Paperback. Etat : Very Good. Unread. In stock ready to dispatch from the UK.

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Etat : New. pp. 298 47 Illus.

Etat : New. pp. 298 Index.

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Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Taschenbuch. Etat : Neu. Neuware - Unlock the full potential of R-from classical statistical learning to cutting-edge Generative AI. Whether you're a student beginning your data science journey, a researcher conducting advanced analytics, or a working professional building enterprise AI solutions, this book provides a practical, hands-on roadmap to mastering data mining, machine learning, deep learning, and Generative AI using R. Unlike traditional R programming books that focus only on syntax or statistical theory, this comprehensive guide bridges the gap between academic concepts and real-world enterprise applications. Through practical examples, industry case studies, and production-ready R code, you'll learn how modern AI solutions are designed, developed, and deployed. Inside this book, you'll explore: - Build a strong foundation in R programming for data analytics and AI.- Understand regression, classification, clustering, and dimensionality reduction techniques.- Implement decision trees, random forests, boosting, K-Nearest Neighbors, Naïve Bayes, and association rule mining.- Develop deep learning models using neural networks, TensorFlow, and Keras in R.- Learn the fundamentals of Generative AI, Large Language Models (LLMs), prompt engineering, embeddings, and Retrieval-Augmented Generation (RAG).- Apply AI techniques to real-world business problems in banking, healthcare, retail, and customer analytics.- Evaluate model performance using industry-standard metrics and best practices.- Explore Responsible AI, model governance, and enterprise AI implementation strategies.Follow complete, reproducible R code examples and hands-on projects throughout the book. What makes this book different - Practical, project-based learning approach- Enterprise-focused AI and analytics use cases- Step-by-step explanations suitable for beginners and professionals- Modern coverage of Deep Learning and Generative AI with R- Interview questions, exercises, and real-world case studies- Designed for both academic learning and industry applicationWho should read this book Data ScientistsAI & Machine Learning EngineersData AnalystsBusiness AnalystsUniversity StudentsResearchers and Doctoral CandidatesSoftware EngineersBanking and Financial Analytics Professionals>Whether your goal is to build predictive models, create intelligent applications, or understand the future of enterprise AI, this book equips you with the knowledge, practical skills, and confidence to transform data into intelligent decisions. Start your journey today-from statistical learning to enterprise-ready AI-with one of the world's most powerful open-source programming languages. …

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Etat : New. This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. Series: Practical Guides to Biostatistics and Epidemiology. Num Pages: 298 pages, 47 b/w illus. 25 tables. BIC Classification: MBNS. Category: (U) Tertiary Education (US: College). Dimension: 247 x 175 x 19. Weight in Grams: 596. . 2011. Illustrated. paperback. . . . . …

Etat : New. This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. Series: Practical Guides to Biostatistics and Epidemiology. Num Pages: 298 pages, 47 b/w illus. 25 tables. BIC Classification: MBNS. Category: (U) Tertiary Education (US: College). Dimension: 247 x 175 x 19. Weight in Grams: 596. . 2011. Illustrated. paperback. . . . . Books ship from the US and Ireland. …

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hardcover. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Etat : New. pp. 388.

Etat : New. pp. 388.

Etat : New.

Etat : New.

Etat : New.

Etat : New. This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. Series: Practical Guides to Biostatistics and Epidemiology. Num Pages: 298 pages, 47 b/w illus. 25 tables. BIC Classification: MBNS. Category: (U) Tertiary Education (US: College). Dimension: 253 x 174 x 24. Weight in Grams: 754. . 2011. 1st Edition. hardcover. . . . . …

Etat : As New. Unread book in perfect condition.

Taschenbuch. Etat : Neu. Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches | Theory and Practical Applications | Fouzi Harrou (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Elsevier | EAN 9780128193655 | Verantwortliche Person für die EU: Elsevier B.V., Radarweg 29, 1043 NX AMSTERDAM, NIEDERLANDE, productsafety[at]elsevier[dot]com | Anbieter: preigu. …

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. Series: Practical Guides to Biostatistics and Epidemiology. Num Pages: 298 pages, 47 b/w illus. 25 tables. BIC Classification: MBNS. Category: (U) Tertiary Education (US: College). Dimension: 253 x 174 x 24. Weight in Grams: 754. . 2011. 1st Edition. hardcover. . . . . Books ship from the US and Ireland. …

Etat : New. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven bas.

Hardcover. Etat : new. Hardcover. This contributed volume offers practical implementation strategies for statistical learning and data science techniques, with fully peer-reviewed papers that embody insights and experiences gathered within the LISA 2020 Global Network. Through a series of compelling case studies, readers are immersed in practical methodologies, real-world applications, and innovative approaches in statistical learning and data science.Topics covered in this volume span a wide array of applications, including machine learning in health data analysis, deep learning models for precipitation modeling, interpretation techniques for machine learning models in BMI classification for obesity studies, as well as a comparative analysis of sampling methods in machine learning health applications. By addressing the evolving landscape of data analytics in many ways, this volume serves as a valuable resource for practitioners, researchers, and students alike.The LISA 2020 Global Network is dedicated to enhancing statistical and data science capabilities in developing countries through the establishment of collaboration laboratories, also known as stat labs. These stat labs function as engines for development, nurturing the next generation of collaborative statisticians and data scientists while providing essential research infrastructure for researchers, data producers, and decision-makers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Etat : Neu. Practical Statistical Learning and Data Science Methods | Case Studies from LISA 2020 Global Network, USA | O. Olawale Awe (u. a.) | Taschenbuch | STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health | xxix | Englisch | 2025 | Springer | EAN 9783031722172 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volume offers practical implementation strategies for statistical learning and data science techniques, with fully peer-reviewed papers that embody insights and experiences gathered within the LISA 2020 Global Network. Through a series of compelling case studies, readers are immersed in practical methodologies, real-world applications, and innovative approaches in statistical learning and data science.Topics covered in this volume span a wide array of applications, including machine learning in health data analysis, deep learning models for precipitation modeling, interpretation techniques for machine learning models in BMI classification for obesity studies, as well as a comparative analysis of sampling methods in machine learning health applications. By addressing the evolving landscape of data analytics in many ways, this volume serves as a valuable resource for practitioners, researchers, and students alike.The LISA 2020 Global Network is dedicated to enhancing statistical and data science capabilities in developing countries through the establishment of collaboration laboratories, also known as 'stat labs.' These stat labs function as engines for development, nurturing the next generation of collaborative statisticians and data scientists while providing essential research infrastructure for researchers, data producers, and decision-makers.…

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volume offers practical implementation strategies for statistical learning and data science techniques, with fully peer-reviewed papers that embody insights and experiences gathered within the LISA 2020 Global Network. Through a series of compelling case studies, readers are immersed in practical methodologies, real-world applications, and innovative approaches in statistical learning and data science.Topics covered in this volume span a wide array of applications, including machine learning in health data analysis, deep learning models for precipitation modeling, interpretation techniques for machine learning models in BMI classification for obesity studies, as well as a comparative analysis of sampling methods in machine learning health applications. By addressing the evolving landscape of data analytics in many ways, this volume serves as a valuable resource for practitioners, researchers, and students alike.The LISA 2020 Global Network is dedicated to enhancing statistical and data science capabilities in developing countries through the establishment of collaboration laboratories, also known as 'stat labs.' These stat labs function as engines for development, nurturing the next generation of collaborative statisticians and data scientists while providing essential research infrastructure for researchers, data producers, and decision-makers.…

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