Machine Learning for Hackers. Case Studies and Algorithms to Get You Started - Drew Conway, John Myles White

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Książkę Machine Learning for Hackers. Case Studies and Algorithms to Get You Started można najtaniej kupić w sklepie internetowym ebookpoint.pl za jedyne 143.65zł.
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Machine Learning for Hackers. Case Studies and Algorithms to Get You Started - Drew Conway, John Myles White Machine Learning for Hackers. Case Studies and Algorithms to Get You Started Drew Conway, John Myles White ebookpoint
ebookpoint.pl od 143.65PLN
KUP KSIĄŻKĘ
Machine Learning for Hackers. Case Studies and Algorithms to Get You Started - Drew Conway, John Myles White

Machine Learning for Hackers. Case Studies and Algorithms to Get You Started za 143.65 zł

Cena: 143.65
Sklep: ebookpoint.pl
Kup teraz Machine Learning for Hackers. Case Studies and Algorithms to Get You Started za 143.65 zł w księgarni ebookpoint.pl
Wydawca: ebookpoint
Kategoria: bez kategorii
ISBN: 9781449330538
Waga: b/d kg
Rok wydania: 2020

If you...re an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you...ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a “whom to follow" recommendation system from Twitter data

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