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Online Learning with R: Build Self-Updating Machine Learning Models with Real-Time Data and Continuous Prediction
Paperback
Series: Real-World Data Science with R, Book 4
General ComputersProbability & Statistics
Publisher: Independently Published
Published: Mar 26 2026
Pages: 114
Weight: 0.36
Height: 0.24 Width: 6.00 Depth: 9.00
Language: English
ONLINE LEARNING WITH R 2026: Build Self-Updating Machine Learning Models with Real-Time Data and Continuous Prediction
No GPS? No problem. With this book, you'll always know what's ahead.
Most machine learning books teach you how to build models that work once. This book shows you how to build systems that keep working no matter how fast the data changes.
In real-world environments, data never stops. User behavior shifts. Markets evolve. Sensors stream continuously. Static models fail quietly while decisions become increasingly wrong. The problem isn't your model, it's the assumption that the world stands still.
This book breaks that assumption.
ONLINE LEARNING WITH R 2026 is built for practitioners who want to move beyond batch workflows and design systems that learn continuously. It focuses on real-world implementation, not theory-showing how to build machine learning pipelines that update themselves, adapt to change, and remain reliable in production.
Inside, you'll learn how to:
- Build self-updating machine learning models that learn from streaming data
- Design real-time data pipelines that don't break under pressure
- Detect and respond to concept drift before it destroys performance
- Engineer features that remain consistent in live systems
- Deploy low-latency prediction APIs using R
- Evaluate models continuously without relying on outdated validation methods
- Build fault-tolerant systems that recover from failure automatically
- Apply online learning to time series, anomaly detection, and real-world forecasting
- Implement complete end-to-end systems from ingestion to live deployment
This is not a theoretical guide. It is a system-building manual.
Every chapter is designed to reflect how machine learning actually operates in production continuous, imperfect, and constantly evolving. The examples focus on real use cases such as fraud detection, recommendation systems, predictive maintenance, and demand forecasting.
If you are a data scientist, machine learning engineer, or analyst ready to move beyond static models and build systems that adapt in real time, this book gives you the structure, tools, and mindset to do it.
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