Linear Models & Gradient Descent: Managing Linear Models
Intermediate
- 11 videos | 47m 46s
- Includes Assessment
- Earns a Badge
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
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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
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53s
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7m 9sFind out how to define a linear model and the various classification of linear models that are used in predictive analytics. FREE ACCESS
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3. Linear Modeling Approach4m 24sAfter 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). FREE ACCESS
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4. Generalized Linear Model2m 55sIn 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). FREE ACCESS
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5. ANOVA and ANCOVA3m 39sIn this video, learn how to compare the differences between the ANOVA and ANCOVA approaches to statistical testing. FREE ACCESS
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6. Linear Model Implementation3m 49sLearn about the implementation of linear regression models using Scikit-learn. FREE ACCESS
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7. Bias, Variance and Regularization6m 51sUpon completion of this video, you will be able to describe the concepts of bias, variance and regularization and their usages in evaluating predictive models. FREE ACCESS
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8. Ensemble Techniques7m 28sIn this video, you will define the concept of ensemble techniques and illustrate how bagging and boosting algorithms can be used to improve predictions. FREE ACCESS
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9. Bagging Implementation3m 35sDuring this video, you will learn how to implement bagging algorithms with the random forest approach using Scikit-learn. FREE ACCESS
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10. Implementing Boosting Algorithm4m 10sIn this video, you will learn how to implement boosting ensemble algorithms using the Adaboost classifier in Python. FREE ACCESS
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11. Exercise: Linear Models and Ensemble2m 55sUpon 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. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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