Machine learning requires strong statistical foundations. In this module, we solidify that groundwork by reviewing probability concepts such as important distributions, Bayes' Rule, and conditional expectation. We then move on to rigorous statistical analysis — parameter estimation, hypothesis testing, p-values, z-scores, and other core concepts in statistical inference. We extend this basis of statistical knowledge into machine learning basics such as regression, regularization, overfitting, and important learning metrics. Students put these concepts into practice on real-world datasets using Python’s stats-oriented libraries to ask interesting, relevant questions and draw concrete inferences from population data.
