# Linear Models & Gradient Descent: Managing Linear Models

Intermediate
• 11 videos | 47m 46s
• Includes Assessment
Rating 3.8 of 8 users (8)
Explore the concept of machine learning linear models, classifications of linear models, and prominent statistical approaches used to implement linear models. This 11-video course also explores the concepts of bias, variance, and regularization. Key concepts covered here include learning about linear models and various classifications used in predictive analytics; learning different statistical approaches that are used to implement linear models [single regression, multiple regression and analysis of variance (ANOVA)]; and various essential components of a generalized linear model (random component, linear predictor and link function). Next, discover differences between the ANOVA and analysis of covariance (ANCOVA) approaches of statistical testing; learn about implementation of linear regression models by using Scikit-learn; and learn about the concepts of bias, variance, and regularization and their usages in evaluating predictive models. Learners explore the concept of ensemble techniques and illustrate how bagging and boosting algorithms are used to manage predictions, and learn to implement bagging algorithms with the approach of random forest by using Scikit-learn. Finally, observe how to implement boosting ensemble algorithms by using Adaboost classifier in Python.

## WHAT YOU WILL LEARN

• Discover the key concepts covered in this course
Define linear model and the various classification of linear models that are used in predictive analytics
Recognize the different statistical approaches that are used to implement linear models (single regression, multiple regression and anova)
Define generalized linear model and the various essential components of generalized linear model (random component, linear predictor and link function)
Compare the differences between the anova and ancova approaches of statistical test
Demonstrate the implementation of linear regression models using scikit-learn
• Describe the concept of bias, variance and regularization and their usages in evaluating predictive models
Define the concept of ensemble techniques and illustrate how bagging and boosting algorithms are used to manage predictions
Implement bagging algorithms with the approach of random forest using scikit-learn
Implement boosting ensemble algorithms using adaboost classifier in python
List the classifications of linear models, recall the essential components of generalized linear models, and implement boosting algorithm using adaboost classifier

## IN THIS COURSE

• Find out how to define a linear model and the various classification of linear models that are used in predictive analytics.
• 3.  Linear Modeling Approach
After completing this video, you will be able to recognize the different statistical approaches that are used to implement linear models (single regression, multiple regression, and ANOVA).
• 4.  Generalized Linear Model
In this video, learn how to define a generalized linear model and the various essential components of a generalized linear model (random component, linear predictor and link function).
• 5.  ANOVA and ANCOVA
In this video, learn how to compare the differences between the ANOVA and ANCOVA approaches to statistical testing.
• 6.  Linear Model Implementation
Learn about the implementation of linear regression models using Scikit-learn.
• 7.  Bias, Variance and Regularization
Upon completion of this video, you will be able to describe the concepts of bias, variance and regularization and their usages in evaluating predictive models.
• 8.  Ensemble Techniques
In this video, you will define the concept of ensemble techniques and illustrate how bagging and boosting algorithms can be used to improve predictions.
• 9.  Bagging Implementation
During this video, you will learn how to implement bagging algorithms with the random forest approach using Scikit-learn.
• 10.  Implementing Boosting Algorithm
In this video, you will learn how to implement boosting ensemble algorithms using the Adaboost classifier in Python.
• 11.  Exercise: Linear Models and Ensemble
Upon completion of this video, you will be able to list the classifications of linear models, recall the essential components of generalized linear models, and implement the boosting algorithm using the Adaboost classifier.

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