Linear Regression Models: Introduction

Machine Learning    |    Beginner
  • 13 videos | 1h 18m 38s
  • Includes Assessment
  • Earns a Badge
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

  • 2m 23s
  • 8m 55s
    In this video, you will define what regression is and recall how it can be used to represent a relationship between two variables. FREE ACCESS
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    3.  Reasons to Use Regression
    7m 17s
    In this video, you will learn how to identify the applications of regression and why it is used to make predictions. FREE ACCESS
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    4.  Regression Loss: Least Square Error
    5m 50s
    After completing this video, you will be able to describe how to evaluate the quality of a regression model by measuring its loss. FREE ACCESS
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    5.  Capturing Variance in Regression
    7m 40s
    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. FREE ACCESS
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    6.  Prediction Using Regression
    3m 36s
    Upon completion of this video, you will be able to describe the technique used to make predictions with regression models. FREE ACCESS
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    7.  Introduction to Deep Learning
    7m 24s
    Find out how to compare classic ML and deep learning techniques to perform a regression. FREE ACCESS
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    8.  The Architecture of Neural Networks
    5m 3s
    In this video, you will identify the various components of a neural network, such as neurons and layers, and how they fit together. FREE ACCESS
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    9.  Neurons: The Building Blocks of a Neural Network
    7m 49s
    Upon completion of this video, you will be able to recall the two types of functions used in a neuron and their individual roles. FREE ACCESS
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    10.  Linear Regression Using a Single Neuron
    3m 15s
    After completing this video, you will be able to describe the configurations required to use a neuron for linear regression. FREE ACCESS
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    11.  Training a Neural Network
    6m 45s
    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. FREE ACCESS
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    12.  Gradient Descent Optimization
    7m 40s
    In this video, you will learn how to define the technique of gradient descent optimization in order to find the optimal parameters for a neural network. FREE ACCESS
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    13.  Exercise: Introduction to Linear Regression
    5m 1s
    After completing this video, you will be able to recall key concepts of linear regression and deep learning. FREE ACCESS

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