by Max Kuhn
Amazon availability
Recommended by 1 source and appears in Statistics, Math, and Math Science.
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 Statistics, Math, and Math Science.
People and public figures who have recommended this book.
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”
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Consider First Course in Probability, A by Sheldon Ross.
“Sheldon Ross’s First Course in Probability reads like a clear, calculus-based undergraduate textbook: definitions, step-by-step derivations, and many worked examples aimed at building formal comfort with probability. What works best is its mathematical clarity — it pushes you through proofs and algebra so you understand why common distributions and counting arguments work. The main limitation is tone and pacing: chapters can feel terse and formula-heavy, and the bundled diskette/tooling feels dated for readers expecting modern software support.”
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.
