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Machine Learning Intermediate

Regression Models

A COMPLETE beginner-to-advanced Machine Learning course on Regression Models using Python and Scikit-learn. Learn Linear Regression, Random Forests, and more.

20 chapters 1h 57m 4.5 (169)
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What you'll learn

  • Introduction to Regression Models
  • Setting Up Python and Machine Learning Environment
  • Python Basics for Regression Analysis
  • NumPy, Pandas, and Data Preparation
  • Understanding Regression Fundamentals
  • Simple Linear Regression
  • Multiple Linear Regression
  • Regression Assumptions Explained

Course content

20 chapters Β· 1h 57m
  1. 1 Introduction to Regression Models 6 min
  2. 2 Setting Up Python and Machine Learning Environment 6 min
  3. 3 Python Basics for Regression Analysis 5 min
  4. 4 NumPy, Pandas, and Data Preparation 6 min
  5. 5 Understanding Regression Fundamentals 6 min
  6. 6 Simple Linear Regression 6 min
  7. 7 Multiple Linear Regression 6 min
  8. 8 Regression Assumptions Explained 6 min
  9. 9 Data Preprocessing for Regression 6 min
  10. 10 Feature Engineering and Selection 6 min
  11. 11 Polynomial Regression 6 min
  12. 12 Ridge Regression and Lasso Regression 6 min
  13. 13 Elastic Net Regression 6 min
  14. 14 Decision Tree Regression 6 min
  15. 15 Random Forest Regression 6 min
  16. 16 Support Vector Regression (SVR) 5 min
  17. 17 Model Evaluation Metrics for Regression 6 min
  18. 18 Hyperparameter Tuning and Cross Validation 6 min
  19. 19 Saving, Deploying, and Using Regression Models 6 min
  20. 20 Final Project - Build Real-World Regression Applications 5 min