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