Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Data Mining, Deep Learning, and Generative AI with R: A Practical Guide from Statistical Learning to Enterprise AI

Data Mining, Deep Learning, and Generative AI with R: A Practical Guide from Statistical Learning to Enterprise AI

Biswas, Ayan

Paperback

General Computers

ISBN13: 9798186955962
Publisher: Independently Published
Published: Jul 12 2026
Pages: 134
Weight: 0.31
Height: 0.29 Width: 5.00 Depth: 8.00
Language: English
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:

  1. Build a strong foundation in R programming for data analytics and AI.
  2. Understand regression, classification, clustering, and dimensionality reduction techniques.
  3. Implement decision trees, random forests, boosting, K-Nearest Neighbors, Naïve Bayes, and association rule mining.
  4. Develop deep learning models using neural networks, TensorFlow, and Keras in R.
  5. Learn the fundamentals of Generative AI, Large Language Models (LLMs), prompt engineering, embeddings, and Retrieval-Augmented Generation (RAG).
  6. Apply AI techniques to real-world business problems in banking, healthcare, retail, and customer analytics.
  7. Evaluate model performance using industry-standard metrics and best practices.
  8. 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 application

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