Linear Regression Models: Introduction to Linear Regression

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

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Overview/Description
Machine learning (ML) is everywhere these days, often invisible to most of us. In this 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 and how they fit together. You will learn the two types of functions used in a neuron and their individual roles; how to calculate the optimal weights and biases of a neural network; and how to find the 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