Linear Models & Gradient Descent: Gradient Descent and Regularization

Intermediate
  • 12 Videos | 58m 22s
  • Includes Assessment
  • Earns a Badge
Likes 7 Likes 7
Explore the features of simple and multiple regression, implement simple and multiple regression models, and explore concepts of gradient descent and regularization and different types of gradient descent and regularization. Key concepts covered in this 12-video course include characteristics of the prominent types of linear regression; essential features of simple and multiple regressions and how they are used to implement linear models; and how to implement simple regression models by using Python libraries for machine learning solutions. Next, observe how to implement multiple regression models in Python by using Scikit-learn and StatsModels; learn the different types of gradient descent; and see how to classify the prominent gradient descent optimization algorithms from the perspective of their mathematical representation. Learn how to implement a simple representation of gradient descent using Python; how to implement linear regression by using mini-batch gradient descent to compute hypothesis and predictions; and learn the benefits of regularization and the objectives of L1 and L2 regularization. Finally, learn how to implement L1 and L2 regularization of linear models by using Scikit-learn.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    list and describe the characteristics of the prominent types of linear regression
    describe the essential features of simple and multiple regressions and how they're used to implement linear models
    demonstrate how to implement simple regression models using Python libraries
    implement multiple regression models in Python using Scikit-learn and StatsModels
    define gradient descent and the different types of gradient descent
  • classify the prominent gradient descent optimization algorithms from the perspective of their mathematical representation
    implement a simple representation of gradient descent using Python
    implement linear regression using mini-batch gradient descent to compute hypothesis and predictions
    describe the benefits of regularization and the objective of L1 & L2 regularization
    demonstrate how to implement L1 and L2 regularization of linear models using Scikit-learn
    recall the essential features of simple and multiple regression, implement a simple regression model using Python and implement L1 regularization using Scikit-learn

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    52s
    UP NEXT
  • Playable
    2. 
    Types of Linear Regression
    4m 38s
  • Locked
    3. 
    Simple and Multiple Regression
    5m 2s
    FREE ACCESS
  • Locked
    4. 
    Implementing Simple Regression
    3m 25s
    FREE ACCESS
  • Locked
    5. 
    Implementing Multiple Regression
    3m 18s
    FREE ACCESS
  • Locked
    6. 
    Gradient Descent and Types
    3m 38s
    FREE ACCESS
  • Locked
    7. 
    Gradient Descent Optimization Algorithms
    5m 28s
    FREE ACCESS
  • Locked
    8. 
    Implementing Gradient Descent
    6m 10s
    FREE ACCESS
  • Locked
    9. 
    Implementing Mini Batch Gradient Descent
    4m 47s
    FREE ACCESS
  • Locked
    10. 
    Regularization Types
    4m 57s
    FREE ACCESS
  • Locked
    11. 
    Implementing L1 & L2 Regularization
    6m 11s
    FREE ACCESS
  • Locked
    12. 
    Exercise: Regression and Gradient Descent
    4m 56s
    FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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