This module introduces learners to the essential concepts of sampling methods and statistical data types in Machine Learning. It explores systematic, cluster, and stratified sampling techniques, while also distinguishing between qualitative, quantitative, discrete, continuous, nominal, and ordinal data. By mastering these foundations, learners will understand how data collection and classification impact the accuracy, reliability, and effectiveness of machine learning models.

This module provides a comprehensive foundation in probability theory, random variables, and linear algebra concepts essential for machine learning. Learners will explore probability fundamentals such as conditional probability, independence, and the law of total probability, then advance into discrete and continuous distributions including Bernoulli, geometric, and normal distributions. The module also introduces linear algebra essentials—matrices, transposes, and determinants—equipping learners with mathematical tools required to build and analyze machine learning models effectively.

This module equips learners with the statistical foundations required to test hypotheses, interpret confidence intervals, and apply advanced inferential techniques in machine learning. Learners will explore error types, critical value and p-value approaches, tail tests, and confidence intervals. The module then advances into applied inferential statistics with t-tests, Chi-square tests, and goodness of fit measures, as well as the interpretation of covariance. By the end, learners will be able to conduct robust statistical testing and evaluate data relationships with accuracy.