ISBN: 9798253823200 - online learning with r: build self-updating machine learning models with real-time data and continuous prediction: 4 (real-world data science with r) (7 resultados)

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback. Condición: new. Paperback. ONLINE LEARNING WITH R 2026: Build Self-Updating Machine Learning Models with Real-Time Data and Continuous PredictionNo 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 dataDesign real-time data pipelines that don't break under pressureDetect and respond to concept drift before it destroys performanceEngineer features that remain consistent in live systemsDeploy low-latency prediction APIs using REvaluate models continuously without relying on outdated validation methodsBuild fault-tolerant systems that recover from failure automaticallyApply online learning to time series, anomaly detection, and real-world forecastingImplement complete end-to-end systems from ingestion to live deploymentThis 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. ONLINE LEARNING WITH R 2026: Build Self-Updating Machine Learning Models with Real-Time Data and Continuous PredictionNo 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 dataDesign real-time data pipelines that don't break under pressureDetect and respond to concept drift before it destroys performanceEngineer features that remain consistent in live systemsDeploy low-latency prediction APIs using REvaluate models continuously without relying on outdated validation methodsBuild fault-tolerant systems that recover from failure automaticallyApply online learning to time series, anomaly detection, and real-world forecastingImplement complete end-to-end systems from ingestion to live deploymentThis 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …