• Online, Self-Paced
Course Description

Machine learning includes many different fields that focus on different problems. In this course, you will learn what machine learning is and the fundamentals of supervised learning.

Learning Objectives

Introducing Machine Learning

  • start the course
  • define machine learning and how it can be used to solve a variety of problems
  • define supervised machine learning
  • describe the fundamentals of building machine learning models to solve a problem
  • describe overfitting, how it can be a problem, and how to mitigate it
  • evaluate machine learning models and compare them

Simple Models

  • define the linear regression model for one and multiple variable problems
  • describe the gradient descent algorithm for training linear regression models
  • describe the k-nearest neighbor model and how to learn it
  • describe decision tree models and how to learn decision trees using the C4.5 algorithm

Machine Learning in Python

  • set up scikit-learn for Python
  • import data, and perform basic tasks with scikit-learn for Python
  • use scikit-learn to fit a linear regression model to a dataset
  • use scikit-learn's k-nearest neighbor model
  • use scikit-learn to fit a decision tree model to a dataset
  • use scikit-learn and GraphViz to generate a decision tree model from a dataset
  • use scikit-learn to calculate the precision and the recall of different machine learning models in Python

Practice: scikit-learn

  • fit a linear regression model to a dataset with scikit-learn and 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.