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Regression Analysis
  • Level Professional
  • المدة 40 ساعات hours
  • الطبع بواسطة University of Colorado Boulder
  • Offered by

عن

The "Regression Analysis" course equips students with the fundamental concepts of one of the most important supervised learning methods, regression. Participants will explore various regression techniques and learn how to evaluate them effectively. Additionally, students will gain expertise in advanced topics, including polynomial regression, regularization techniques (Ridge, Lasso, and Elastic Net), cross-validation, and ensemble methods (bagging, boosting, and stacking). Through interactive tutorials and practical case studies, students will gain hands-on experience in applying regression analysis to real-world data scenarios. By the end of this course, students will be able to: 1. Understand the principles and significance of regression analysis in supervised learning. 2. Grasp the concepts and applications of linear regression and its interpretation in real-world datasets. 3. Explore polynomial regression to capture nonlinear relationships between variables. 4. Apply regularization techniques (Ridge, Lasso, and Elastic Net) to prevent overfitting and improve model generalization. 5. Implement cross-validation methods to assess model performance and optimize hyperparameters. 6. Comprehend ensemble methods (bagging, boosting, and stacking) and their role in enhancing regression model accuracy. 7. Evaluate and compare the performance of different regression models using appropriate metrics. 8. Apply regression analysis techniques to real-world case studies, making data-driven decisions. Throughout the course, students will actively engage in tutorials and case studies, strengthening their regression analysis skills and gaining practical experience in applying regression techniques to diverse datasets. By achieving the learning objectives, participants will be well-equipped to excel in regression analysis tasks and make informed decisions using regression models.

الوحدات

Introduction to Regression and Linear Regression

    • 1 Videos
    • 4 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Linear Regression Quiz

1 Discussions

  • Linear Regression Exploration Exercise

1 Videos

  • Introduction to Regression and Linear Regression

4 Readings

  • Assessment Strategy
  • Activity Strategy
  • Linear Regression Demo
  • Linear Regression Case Study

Polynomial Regression

    • 1 Videos
    • 2 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Polynomial Regression Quiz

1 Discussions

  • Polynomial Regression Exploration Exercise

1 Videos

  • Polynomial Regression

2 Readings

  • Polynomial Regression Demo
  • Polynomial Regression Case Study

Regularization

    • 1 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Regularization Quiz

1 Discussions

  • Regularization Exploration Exercise

1 Videos

  • Regularization

3 Readings

  • Regularization Demo
  • Regularization Case Study - CA Housing Price
  • Regularization Case Study

Evaluation and Cross Validation

    • 1 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Evaluation and Cross Validation Quiz

1 Discussions

  • Evaluation and Cross Validation Exploration Exercise

1 Videos

  • Cross Validation

3 Readings

  • Evaluation and Cross Validation Demo
  • Cross Validation Case Study - CA Housing Price
  • Evaluation and Cross Validation Case Study

Ensemble Methods

    • 1 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Ensemble Methods Quiz

1 Discussions

  • Ensemble Methods Exploration Exercise

1 Videos

  • Ensemble Methods

3 Readings

  • Ensemble Methods Demo
  • Ensemble Case Study - CA Housing Price
  • Ensemble Methods Case Study

Case Study

    • 2 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Self Reflection

1 Discussions

  • Regression Analysis Exploration Exercise

2 Readings

  • Regression Analysis Case Study - Demo
  • Regression Analysis Case Study

Auto Summary

Unlock the power of regression with this comprehensive course in Data Science & AI, led by Coursera. Dive into key concepts, from linear regression to advanced techniques like Ridge, Lasso, and Elastic Net, and ensemble methods. Perfect for professionals, this 2400-minute course offers hands-on experience through interactive tutorials and real-world case studies. Available with Starter and Professional subscriptions, it equips learners to master regression analysis and make data-driven decisions confidently.

Di Wu
Instructor

Di Wu