# Linear Regression Models: Introduction

Machine Learning    |    Beginner
• 13 videos | 1h 18m 38s
• Includes Assessment
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.

## WHAT YOU WILL LEARN

• 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

## IN THIS COURSE

• In this video, you will define what regression is and recall how it can be used to represent a relationship between two variables.
• 3.  Reasons to Use Regression
In this video, you will learn how to identify the applications of regression and why it is used to make predictions.
• 4.  Regression Loss: Least Square Error
After completing this video, you will be able to describe how to evaluate the quality of a regression model by measuring its loss.
• 5.  Capturing Variance in Regression
Upon completion of this video, you will be able to recognize the specific relationship which needs to exist between the input and output of a regression model.
• 6.  Prediction Using Regression
Upon completion of this video, you will be able to describe the technique used to make predictions with regression models.
• 7.  Introduction to Deep Learning
Find out how to compare classic ML and deep learning techniques to perform a regression.
• 8.  The Architecture of Neural Networks
In this video, you will identify the various components of a neural network, such as neurons and layers, and how they fit together.
• 9.  Neurons: The Building Blocks of a Neural Network
Upon completion of this video, you will be able to recall the two types of functions used in a neuron and their individual roles.
• 10.  Linear Regression Using a Single Neuron
After completing this video, you will be able to describe the configurations required to use a neuron for linear regression.
• 11.  Training a Neural Network
Upon completion of this video, you will be able to list the steps involved in calculating the optimal weights and biases of a neural network.