Supervised Machine Learning for Big Data
Build Predictive Models That Scale with Your Data
This guide takes you through the complete process of building supervised machine learning models that work at scale. You'll start by understanding how to structure your data pipeline from databases to training algorithms, then progress through essential preprocessing techniques that transform raw data into model-ready features. The book covers the core supervised learning algorithms—regression, classification, and ensemble methods—with practical guidance on when and how to use each. You'll learn how to handle the unique challenges of big data, including distributed training, memory optimization, and computational efficiency. Throughout, you'll discover how to evaluate your models rigorously, tune hyperparameters effectively, and deploy systems that maintain performance in production. Whether you're working with millions or billions of records, this book provides the frameworks and techniques to build models that deliver real business value.
Est. Length
238 pages (11 chapters)
Language
English
Publication date
Dec 2, 2025
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