This module covers statistical concepts essential for building and interpreting machine learning models. Learners will review core measures such as variance, correlation, R-squared, and standard error while identifying common statistical mistakes. The module also extends to advanced topics including linear regression, statistical assumptions, and interpretation of outputs, equipping learners with the ability to analyze data with confidence.

This module focuses on probability distributions and hypothesis testing, both critical to statistical inference. Learners will examine discrete and continuous probability distributions, variance-covariance structures, and hypothesis rejection criteria. The module also introduces classical distributions such as t, chi-square, and Poisson, along with visualization techniques for testing data assumptions and interpreting results.

This module introduces core machine learning algorithms, focusing on regression, classification, decision trees, and ensemble methods. Learners will explore K-Nearest Neighbors (KNN), generalized regression models, decision tree classifiers, and the use of pruning to improve performance. The module concludes with ensemble learning techniques, including random forests and boosting, for building powerful predictive models.