000			02984cam a2200277zu 4500
001			88953901
003			FRCYB88953901
005			20250108003726.0
006			m o d
007			cr un
008			250108s2024 fr | o|||||0|0|||eng d
020			_a9781835882061
035			_aFRCYB88953901
040			_aFR-PaCSA _ben _c _erda
100	1		_aTheobald, Oliver
245	0	1	_aMachine Learning: Make Your Own Recommender System _bBuild Your Recommender System with Machine Learning Insights _c['Theobald, Oliver']
264		1	_bPackt Publishing _c2024
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aTheobald, Oliver
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88953901 _qtext/html _a
520			_aLaunch 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.
999			_c78882 _d78882