Machine Learning: Make Your Own Recommender System (notice n° 78882)

détails MARC
000 -LEADER
fixed length control field 02984cam a2200277zu 4500
003 - CONTROL NUMBER IDENTIFIER
control field FRCYB88953901
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250108003726.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250108s2024 fr | o|||||0|0|||eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781835882061
035 ## - SYSTEM CONTROL NUMBER
System control number FRCYB88953901
040 ## - CATALOGING SOURCE
Original cataloging agency FR-PaCSA
Language of cataloging en
Transcribing agency
Description conventions rda
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Theobald, Oliver
245 01 - TITLE STATEMENT
Title Machine Learning: Make Your Own Recommender System
Remainder of title Build Your Recommender System with Machine Learning Insights
Statement of responsibility, etc. ['Theobald, Oliver']
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer Packt Publishing
Date of production, publication, distribution, manufacture, or copyright notice 2024
300 ## - PHYSICAL DESCRIPTION
Extent p.
336 ## - CONTENT TYPE
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type code c
Source rdamdedia
338 ## - CARRIER TYPE
Carrier type code c
Source rdacarrier
520 ## - SUMMARY, ETC.
Summary, etc. Launch into machine learning with our course and learn to create advanced recommender systems, ensuring ethical use and maximizing user satisfaction.Key FeaturesNavigate Scikit-Learn effortlesslyCreate advanced recommender systemsUnderstand ethical AI developmentBook DescriptionWith an introductory overview, the course prepares you for a deep dive into the practical application of Scikit-Learn and the datasets that bring theories to life. From the basics of machine learning to the intricate details of setting up a sandbox environment, this course covers the essential groundwork for any aspiring data scientist. The course focuses on developing your skills in working with data, implementing data reduction techniques, and understanding the intricacies of item-based and user-based collaborative filtering, along with content-based filtering. These core methodologies are crucial for creating accurate and efficient recommender systems that cater to the unique preferences of users. Practical examples and evaluations further solidify your learning, making complex concepts accessible and manageable. The course wraps up by addressing the critical topics of privacy, ethics in machine learning, and the exciting future of recommender systems. This holistic approach ensures that you not only gain technical proficiency but also consider the broader implications of your work in this field. With a final look at further resources, your journey into machine learning and recommender systems is just beginning, armed with the knowledge and tools to explore new horizons.What you will learnBuild data-driven recommender systemsImplement collaborative filtering techniquesApply content-based filtering methodsEvaluate recommender system performanceAddress privacy and ethical considerationsAnticipate future recommender system trendsWho this book is forThis course is ideal for aspiring data scientists and technical professionals with a basic understanding of Python programming and a keen interest in machine learning. This course lays the groundwork for those looking to specialize in building sophisticated recommender systems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Theobald, Oliver
856 40 - ELECTRONIC LOCATION AND ACCESS
Access method Cyberlibris
Uniform Resource Identifier <a href="https://international.scholarvox.com/netsen/book/88953901">https://international.scholarvox.com/netsen/book/88953901</a>
Electronic format type text/html
Host name

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