Probability for Data Science - Course Introduction
In this video, I provide an overview to the probability for data science course. The Probability for Data Science course will provide students and practitioners the necessary background for kick starting their machine learning and data science careers. The course will cover important concepts such as discrete and continuous random variables, statistical independence, and Bayes Rule. Students will also learn how to compute the expectation and variance of random variables. Additionally, the course will also cover specific distributions such as geometric, binomial, Poisson, uniform, exponential, and normal distributions that are key to modeling real-world data generated from day-to-day activities and interactions.
If you want to obtain a certification and a Probability for Data Science Foundations badge from the State University of New York Binghamton based on the videos in this channel, please visit the link. For obtaining the certification, you will need to pass a multiple choice final exam based on these videos. The course also contains self-assessment quizzes to help you prepare for the finals and for obtaining the certificate.
https://www.binghamton.edu/watson/con...
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