Linear Models & Gradient Descent: Gradient Descent and Regularization
12 Videos | 58m 22s - Includes Assessment
- Earns a Badge
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
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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
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1.Course Overview52sUP NEXT
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2.Types of Linear Regression4m 38s
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3.Simple and Multiple Regression5m 2s
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4.Implementing Simple Regression3m 25s
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5.Implementing Multiple Regression3m 18s
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6.Gradient Descent and Types3m 38s
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7.Gradient Descent Optimization Algorithms5m 28s
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8.Implementing Gradient Descent6m 10s
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9.Implementing Mini Batch Gradient Descent4m 47s
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10.Regularization Types4m 57s
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11.Implementing L1 & L2 Regularization6m 11s
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12.Exercise: Regression and Gradient Descent4m 56s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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