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R for Data Science: Implementing Machine Learning Models

by Mary M. Adepoju , Olaoluwa S. Yaya
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Current price ₹1,470.00
Original price ₹1,656.00
Original price ₹1,656.00
Original price ₹1,656.00
(-11%)
₹1,470.00
Current price ₹1,470.00

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Book cover type: Paperback
  • ISBN13: 9798299106329
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 150
  • Original Price: USD 18.0
  • Language: English
  • Edition: N/A
  • Item Weight: 209 grams
  • BISAC Subject(s): Machine Theory

Unlock the power of machine learning in R with R for Data Science: Implementing Machine Learning Models. This comprehensive guide equips data scientists, analysts, and R enthusiasts with the practical skills needed to build, evaluate, and deploy advanced machine learning solutions across domains. Covering both fundamental and advanced topics, this book blends theory, hands-on examples, and real-world workflows to empower readers to harness R's full capabilities.
Learn how to:

  • Preprocess, clean, and transform data for robust analysis.
  • Build predictive models with regression, classification, and time series techniques.
  • Apply natural language processing and text analytics to extract insights from unstructured data.
  • Explore clustering, dimensionality reduction, and anomaly detection in unsupervised learning.
  • Optimize models through hyperparameter tuning, ensemble methods, and stacking strategies.
  • Develop reproducible workflows, pipelines, and deployment-ready solutions in R.
Each chapter is written in a clear, third-person narrative, providing step-by-step guidance without copying existing materials. Whether you are a beginner aiming to understand R's machine learning ecosystem or an experienced practitioner looking to implement integrated, high-performance models, this book provides a roadmap to mastering the art of data-driven decision-making.
Harness the power of R to explore, analyze, and predict like never before. From structured datasets to complex textual information, this book delivers the tools and insights needed to become a proficient R-based machine learning practitioner.

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