Linear Regression Models: Introduction

Machine Learning
  • 13 Videos | 1h 24m 8s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Course Overview
    2m 23s
    UP NEXT
  • Playable
    2. 
    Statistical Tools and Regression
    8m 55s
  • Locked
    3. 
    Reasons to Use Regression
    7m 17s
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    4. 
    Regression Loss: Least Square Error
    5m 50s
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    5. 
    Capturing Variance in Regression
    7m 40s
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    6. 
    Prediction Using Regression
    3m 36s
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    7. 
    Introduction to Deep Learning
    7m 24s
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    8. 
    The Architecture of Neural Networks
    5m 3s
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    9. 
    Neurons: The Building Blocks of a Neural Network
    7m 49s
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    10. 
    Linear Regression Using a Single Neuron
    3m 15s
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    11. 
    Training a Neural Network
    6m 45s
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    12. 
    Gradient Descent Optimization
    7m 40s
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    13. 
    Exercise: Introduction to Linear Regression
    5m 1s

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