# Linear Regression Models: Introduction

Machine Learning    |    Beginner
• 13 Videos | 1h 24m 8s
• Includes Assessment
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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

• 1.
Course Overview
• 2.
Statistical Tools and Regression
• 3.
Reasons to Use Regression
• 4.
Regression Loss: Least Square Error
• 5.
Capturing Variance in Regression
• 6.
Prediction Using Regression
• 7.
Introduction to Deep Learning
• 8.
The Architecture of Neural Networks
• 9.
Neurons: The Building Blocks of a Neural Network
• 10.
Linear Regression Using a Single Neuron
• 11.
Training a Neural Network
• 12.