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Data Science 101: Machine Learning, Part 2


The “How Machine Learning Works” lecture series continues by building on fundamental definitions of statistics. This is needed for any rigorous analysis of models or machine learning algorithms. This time we will talk about joint, conditional probabilities and conditional expectations. Specifically, we will go over an example of conditional probabilities in the real-world and talk about how they impact analysis. We will finally talk about Bayes rule which will set us up to talk about a Bayesian classifier in the following lecture. This lecture is presented by BloomReach engineer Srinath Sridha.



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