Learning Outcomes

In this free course, you will learn how to:

• Define machine learning
• Explain feature scaling and data cleaning
• Outline feature engineering
• Distinguish between parametric and non-parametric models
• Discuss the exploratory data analysis on the Iris dataset
• Contrast categorical variables to numerical variables
• Explain decision trees and implementation
• Define ensemble learning
• Outline bootstrap sampling
• Compare SVM (support vector machines) hard margin to SVM soft margin
• List the various drawbacks of the PCA (principal component analysis)
• Recall the main applications of the PCA