• Online, Self-Paced
Course Description

Machine Learning is everywhere these days, often invisible to most of us. Discover one of the fundamental problems in the world of ML - linear regression. Explore how this is solved using classic ML as well as Neural Networks.

Learning Objectives

Linear Regression Models: Introduction to Linear Regression

  • Course Overview
  • define what regression is and recall how it can be used to represent a relationship between two variables
  • identify the applications of regression and recognize why it is used to make predictions
  • describe how to evaluate the quality of a regression model by measuring its loss
  • recognize the specific relationship which needs to exist between the input and output of a regression model
  • describe the technique used in order to make predictions with regression models
  • compare classic ML and deep learning techniques to perform a regression
  • identify the various components of a neural network such as neurons and layers and how they fit together
  • recall the two types of functions used in a neuron and their individual roles
  • describe the configurations required to use a neuron for linear regression
  • list the steps involved in calculating the optimal weights and biases of a neural network
  • define the technique of gradient descent optimization in order to find the optimal parameters for a neural network
  • recall key concepts of linear regression and deep learning

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.