Feature Engineering Lesson 43 – ML-Based Feature Selection | Dataplexa

Feature Engineering Course

I. Beginner Level
1. Introduction to Feature Engineering 2. What Are Features? 3. Types of Features 4. Numerical Features 5. Categorical Features 6. Date & Time Features 7. Text Features Basics 8. Missing Data 9. Outliers 10. Data Transformations 11. Binning & Discretization 12. Feature Scaling 13. Encoding Basics 14. Feature Construction 15. FE Workflow
II. Intermediate Level
16. Polynomial Features 17. Interaction Features 18. Target Encoding 19. Frequency Encoding 20. Weight of Evidence 21. Rare Label Encoding 22. Outlier-Based Features 23. Transformations for Skewed Data 24. Feature Selection Basics 25. Filter Methods 26. Wrapper Methods 27. Embedded Methods 28. Variance Thresholding 29. Missing Indicator Features 30. Domain-Driven Features 31. Advanced DateTime Features 32. Group-Based Features 33. Rolling Window Features 34. Lag Features 35. FE for Imbalanced Data
III. Advanced Level
36. PCA Feature Reduction 37. FE for Regression 38. FE for Classification 39. FE for NLP 40. FE for Time Series 41. FE for CV 42. Automated Feature Engineering 43. ML-Based Feature Selection 44. Deep Feature Synthesis 45. FE Project
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