Linear Regression Models: Introduction

Machine Learning    |    Beginner
  • 13 videos | 1h 18m 38s
  • Includes Assessment
  • Earns a Badge
Rating 4.6 of 36 users Rating 4.6 of 36 users (36)
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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