# Course details

Linear Regression Models: Introduction to Linear Regression

### Linear Regression Models: Introduction to Linear Regression

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

Overview/Description

Machine learning (ML) is everywhere these days, often invisible to most of us. In this 12-video course, you will discover one of the fundamental problems in the world of ML: linear regression. Explore how this is solved with classic ML as well as neural networks. Key concepts covered here include how regression can be used to represent a relationship between two variables; applications of regression, and why it is used to make predictions; and how to evaluate the quality of a regression model by measuring its loss. Next, learn techniques used to make predictions with regression models; compare classic ML and deep learning techniques to perform a regression; and observe various components of a neural network, such as neurons and layers and how they fit together. You will learn the two types of functions used in a neuron and their individual roles; steps involved in calculating the optimal weights and biases of a neural network; and the technique of gradient descent optimization, needed to find optimal parameters for a neural network.

Expected Duration (hours)
1.3

Lesson 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
• Course Number:
it_mllrmddj_01_enus

Expertise Level
Beginner