Understand the principles and significance of regression analysis in supervised learning.

Implement cross-validation methods to assess model performance and optimize hyperparameters.

Comprehend ensemble methods (bagging, boosting, and stacking) and their role in enhancing regression model accuracy.

Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction.

Apply clustering techniques to diverse datasets for pattern discovery and data exploration.

Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space.

Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection

Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.

Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.

Define the scope and direction of a data analysis project, identifying appropriate techniques and methodologies for achieving project objectives.

Apply various classification and regression algorithms and implement cross-validation and ensemble techniques to enhance the performance of models.

Apply various clustering, dimension reduction association rule mining, and outlier detection algorithms for unsupervised learning models.