Experimental Design & Recommendations

Starting at USD 399.00
4 weeks duration

Learn to generate personalized recommendations based on user data and run statistically valid tests that produce clean, interpretable results.

Experimental Design & Recommendations is a course on Udacity that covers the fundamentals of experiment design, statistical concerns of experimentation, A/B testing, introduction to recommendation engines, and matrix factorization for recommendations. Throughout the course, students will learn how to set up experiments, understand statistical concepts, identify sources of bias, and implement different techniques for creating recommendation engines. The course also includes a project where students will design a recommendation engine using IBM's online data science community. By the end of the course, students will have a solid understanding of experimental design and be able to build effective recommendation engines.

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Udacity is an online learning platform that offers a wide range of courses and programs in various fields such as technology, business, data science, and artificial intelligence. It was founded in 2012 by Sebastian Thrun, David Stavens, and Mike Sokolsky with the aim of providing accessible and affordable education to individuals worldwide. Udacity's courses are designed in collaboration with industry experts and leading companies, ensuring that the content is relevant and up-to-date. The platform offers both self-paced courses and guided programs, allowing learners to choose the learning style that suits them best. Udacity also provides career services and support, including resume reviews, interview preparation, and job placement assistance, to help learners transition into their desired careers.
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