• Online, Self-Paced
Course Description

Supervised learning is one of the most popular techniques in machine learning. In this course, you will learn about more complicated supervised learning models and how to use them to solve problems.

Learning Objectives

Supervised Learning

  • start the course
  • describe the difference between classification and regression models and the use for each of them
  • describe how decision trees can be applied to regression problems
  • describe the CART decision tree learning algorithm and how it's different from C4.5
  • describe the random forests machine learning
  • use scikit-learn to build a random forest model in Python
  • describe the logistic regression model
  • use scikit-learn to fit a logistic regression model
  • describe support vector machine models
  • describe how to use kernel methods with support vector machines to model more complex data
  • use scikit-learn to train and support vector machines in Python
  • describe the Naïve Bayes classifiers and how to train them
  • use scikit-learn to fit a Naïve Bayes classifier in Python

Practice: Supervised Learning with Python

  • describe different supervised learning models in Python

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.