In this course, you will investigate the underlying mechanics of a machine learning algorithm’s prediction accuracy by exploring the bias variance trade-off. You will identify the causes of prediction error by recognizing high bias and variance while learning techniques to reduce the negative impacts these errors have on learning models. Working with ensemble methods, you will implement techniques that improve the results of your predictive models, creating more reliable and efficient algorithms.

These courses are required to be completed prior to starting this course:

• Problem-Solving with Machine Learning
• Estimating Probability Distributions
• Learning with Linear Classifiers
• Decision Trees and Model Selection