Applied Predictive Modeling
by Max Kuhn
Should I read this?
Recommended by 1 source and appears in Machine Learning, Statistics, and Programming.
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problemsolving with real data across a wide variety of applications will aid practitioners who wish to extend their ...
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Why recommended
Recommended by 1 source and appears in Machine Learning, Statistics, and Programming.
Recommended by notable people
People and public figures who have recommended this book.
Recommendation Signals
Recommendation proof is sourced from public posts, interviews, reading lists, and cited references.
Kirk Borne
“Find more than 40 useful #PredictiveModeling articles here at @DataScienceCtrl #abdsc ———— #BigData #DataScience #AI #MachineLearning #Forecasting #Statistics #PredictiveAnalytics ——— +This is the best book on the subject:”
Appears In
Not sure if this is the right fit?
Consider Deep Learning by Ian Goodfellow. Recommended by 10 sources.
“Equation-forward introduction covering probability, linear-algebra foundations, optimization methods, model families, and common architectures. Sections trade short conceptual summaries for formal derivations and algorithm descriptions; occasional practical notes appear but runnable code is rare. Most useful for building a technical picture of why methods behave as they do and for informed follow-up experimentation. Main limitation: dense notation and extended proofs demand slow, focused study, so readers seeking hands-on walkthroughs will be left wanting.”
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Each recommendation is collected from a public source — interviews, articles, or curated lists — and linked to its original URL. Books with many verifiable recommendations from respected people rank higher.
Applied Predictive Modeling
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