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Recommendation systems. Part 6, Introduction to real-world machine learning /
"Recommendation systems are a class of machine learning models with many applications. The idea behind recommendation systems is simple: filtering information to suggest items (anything from clothes to films) to users with the predicted probability that the users will enjoy such items. This cou...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly,
[2017]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 037 | |a CL0500000895 |b Safari Books Online | ||
| 050 | 4 | |a QA76.87 | |
| 049 | |a UAMI | ||
| 100 | 1 | |a Staglianò, Alessandra, |e speaker. | |
| 245 | 1 | 0 | |a Recommendation systems. |n Part 6, |p Introduction to real-world machine learning / |c with Alessandra Staglianò, Angie Ma, and Gary Willis. |
| 246 | 3 | 0 | |a Introduction to real-world machine learning |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly, |c [2017] | |
| 300 | |a 1 online resource (1 streaming video file (38 min., 33 sec.)) | ||
| 336 | |a two-dimensional moving image |b tdi |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 337 | |a video |b v |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 511 | 0 | |a Presenters, Alessandra Staglianò, Angie Ma, and Gary Willis. | |
| 500 | |a Title from title screen (viewed September 28, 2017). | ||
| 500 | |a Date of publication taken from resource description page. | ||
| 500 | |a "Part 6 of 6." | ||
| 520 | |a "Recommendation systems are a class of machine learning models with many applications. The idea behind recommendation systems is simple: filtering information to suggest items (anything from clothes to films) to users with the predicted probability that the users will enjoy such items. This course provides an introduction to recommendation systems. It starts by looking at the applications for these systems with a focus on the big companies whose fortune is built upon them. It then goes through a discussion of the different types of recommendation systems and how to implement them. You'll explore non-personalized systems, association rule learning, collaborative filtering, personalized systems, and the methods used to assess the quality (i.e., how good are the recommendations?) of a recommendation system. Learners should understand basic logic, supervised learning, and statistics."--Resource description page | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 700 | 1 | |a Ma, Angie, |e speaker. | |
| 700 | 1 | |a Willis, Gary, |e speaker. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/videos/~/9781492023999/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||