Expert techniques for predictive modeling, 3rd Edition
by Brett Lantz
Recommended by Kirk Borne
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Amazon availability
Recommended by 1 source and appears in Programming.
Solve realworld data problems with R and machine learning. Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, handson guide by experienced machine ...
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Why recommended
Recommended by 1 source and appears in Programming.
People and public figures who have recommended this book.
Recommendation proof is sourced from public posts, interviews, reading lists, and cited references.
Kirk Borne
“[FREE eBook] Learn Classification & Regression in a Weekend: ———— #BigData #MachineLearning #Algorithms #DataScience #Statistics #DataMining #AI #PredictiveAnalytics #Rstats #DataScientists #abdsc —— Then go deeper with this book”
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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.”
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.
