Linear Regression Models: Introduction to Linear Regression


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/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.



Expected Duration (hours)
1.3

Lesson Objectives

Linear Regression Models: Introduction to Linear Regression

  • 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
  • Course Number:
    it_mllrmddj_01_enus

    Expertise Level
    Beginner