This course offers a thorough exploration of regression analysis, starting from the fundamental principles of least squares to advanced techniques in model optimization and ensemble methods. Students will acquire expertise in modeling relationships between variables using least squares, describing the significance of the line of best fit in regression modeling. Extensive coverage of data preparation techniques such as test/train split and cross-validation ensures accurate model evaluation and generalization.

The course delves into both simple and multiple linear regression, including multidimensional modeling and model evaluation through residual analysis. Additionally, participants will learn various methods for variable selection and model optimization, including regularization techniques like LASSO and Ridge regression. Decision tree algorithms, ensemble methods, and random forests will be thoroughly explored for predictive modeling. By the course’s end, students will possess the skills to construct robust regression models, make informed predictions, and responsibly utilize cloud resources for scalable computing.

• Apply regression analysis principles, data preparation
  techniques, SLR and MLR differentiation, model optimization methods, decision tree algorithms, and cloud infrastructure skills for predictive modeling.
• Implement essential techniques for data and model preparation, including test/train split, cross-validation, and variable selection methods.
• Differentiate between simple linear regression (SLR) and multiple linear regression (MLR), exploring MLR in 2D and 3D spaces, and analyzing MLR results to evaluate model performance.
• Explore and apply techniques for model optimization, including encoding dummy variables, handling categorical data, and implementing regularization methods such as LASSO and Ridge regression.
• Practice implementation of decision tree algorithms, ensemble methods, and random forests, evaluating their workings, differences, and applications in predictive modeling.