Intermediate
• 12 videos | 53m 22s
• Includes Assessment
Rating 3.9 of 7 users (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
• 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

• After completing this video, you will be able to list and describe the characteristics of the prominent types of linear regression.
• 3.  Simple and Multiple Regression
After completing this video, you will be able to describe the essential features of simple and multiple regressions and how to use them to implement linear models.
• 4.  Implementing Simple Regression
In this video, you will learn how to implement simple regression models using Python libraries.
• 5.  Implementing Multiple Regression
In this video, you will learn how to implement multiple regression models in Python using Scikit-learn and StatsModels.
• 6.  Gradient Descent and Types
Learn how to define gradient descent and the different types of gradient descent.
• 7.  Gradient Descent Optimization Algorithms
In this video, you will learn how to classify the prominent gradient descent optimization algorithms from the perspective of their mathematical representation.
In this video, you will learn how to implement a simple representation of gradient descent using Python.
• 9.  Implementing Mini Batch Gradient Descent
In this video, learn how to implement linear regression using mini-batch gradient descent to compute the hypothesis and predictions.
• 10.  Regularization Types
Upon completion of this video, you will be able to describe the benefits of regularization and the objectives of L1 & L2 regularization.
• 11.  Implementing L1 & L2 Regularization
In this video, you will learn how to implement L1 and L2 regularization of linear models using Scikit-learn.
• 12.  Exercise: Regression and Gradient Descent
Upon completion of this video, you will be able to recall the essential features of simple and multiple regression, implement a simple regression model using Python, and implement L1 regularization using Scikit-learn.

## EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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