Data Mining Deep Learning by Biswas Ayan (5 results)
- Softcover
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: 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 practi…cal, 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.
- Softcover
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Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
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Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. Condition: new. Paperback. 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 practic…al, 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, Naive 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 approachEnterprise-focused AI and analytics use casesStep-by-step explanations suitable for beginners and professionalsModern coverage of Deep Learning and Generative AI with RInterview questions, exercises, and real-world case studiesDesigned 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 ProfessionalsAnyone looking to transition into Data Science and Artificial Intelligence using R 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
