Data Mining, Deep Learning, and Generative AI with R : A Practical Guide from Statistical Learning to Enterprise AI
Language: English
Published by Independently Published Jul 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 19.24
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Add to basketItem description from seller
Seller Inventory # 9798186955962
- Title
- Data Mining, Deep Learning, and Generative AI with R : A Practical Guide from Statistical Learning to Enterprise AI
- Author
- Ayan Biswas
- Publisher
- Independently Published Jul 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798186955962
- Item weight
- 142 grams
- Dimensions
- 203x127x7 mm
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.
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 application
Who should read this book?
Data Scientists
AI & Machine Learning Engineers
Data Analysts
Business Analysts
University Students
Researchers and Doctoral Candidates
Software Engineers
Banking and Financial Analytics Professionals
Anyone 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.
"Synopsis" may belong to another edition of this title.
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