Machine Learning for Hackers

Par : Drew Conway, John Myles White
Définitivement indisponible
Cet article ne peut plus être commandé sur notre site (ouvrage épuisé ou plus commercialisé). Il se peut néanmoins que l'éditeur imprime une nouvelle édition de cet ouvrage à l'avenir. Nous vous invitons donc à revenir périodiquement sur notre site.
Disponible dans votre compte client Decitre ou Furet du Nord dès validation de votre commande. Le format Multi-format est :
  • Pour les liseuses autres que Vivlio, vous devez utiliser le logiciel Adobe Digital Edition. Non compatible avec la lecture sur les liseuses Kindle, Remarkable et Sony
Logo Vivlio, qui est-ce ?

Notre partenaire de plateforme de lecture numérique où vous retrouverez l'ensemble de vos ebooks gratuitement

Pour en savoir plus sur nos ebooks, consultez notre aide en ligne ici
C'est si simple ! Lisez votre ebook avec l'app Vivlio sur votre tablette, mobile ou ordinateur :
Google PlayApp Store
  • Nombre de pages322
  • FormatMulti-format
  • ISBN978-1-4493-0378-5
  • EAN9781449303785
  • Date de parution13/02/2012
  • Protection num.NC
  • Infos supplémentairesMulti-format incluant PDF sans p...
  • ÉditeurO'Reilly Media

Résumé

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 text - Use linear regression to predict the number of page views for the top 1, 000 websites - Learn optimization techniques by attempting to break a simple letter cipher - Compare and contrast U.
S. Senators statistically, based on their voting records - Build a "whom to follow" recommendation system from Twitter data
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 text - Use linear regression to predict the number of page views for the top 1, 000 websites - Learn optimization techniques by attempting to break a simple letter cipher - Compare and contrast U.
S. Senators statistically, based on their voting records - Build a "whom to follow" recommendation system from Twitter data