Building Reliable Regression Models with Python
Learn how regression models predict continuous values, how linear, polynomial, regularized, and advanced regressors work, and how to evaluate them with a practical Python workflow.
Learn how regression models predict continuous values, how linear, polynomial, regularized, and advanced regressors work, and how to evaluate them with a practical Python workflow.
Learn how to map software problems to machine learning approaches, compare supervised and unsupervised learning, understand common algorithm families, and plan a practical workflow from data preparation to optimization and evaluation.