Module 2 includes lectures on regression, error evaluation, and model fitness. There is one Coursera Lab assignment on EDA and Visualization. There is also a Module Quiz at the end of this module.

Module 3 includes lectures on linear models, bootstrapping, predictors, and Model F. There is one Coursera Lab assignment on k-NN Regression. There is also a Module Quiz at the end of this module.

Module 4 includes lectures on overfitting, model selection, cross validation, and bias vs. variance. There is one Coursera Lab assignment on Linear Regression. There is also a Module Quiz at the end of this module.

Module 5 includes lectures on unsupervised learning, inter-observational distances, partition-based clustering, hierarchical clustering, diagnostics, optimization, and density-based clustering. There is one Coursera Lab assignment on Dimensionality Reduction. There is also a Module Quiz at the end of this module.

Module 6 includes lectures on outliers, statistical-based detection, deviation-based detection, and distance-based detection. There is one Coursera Lab assignment on Outlier Detection, Model Selection, and Cross Validation. There is also a Module Quiz at the end of this module.