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Liquidity Regressors in Python

Practice splitting data into training and testing sets, constructing model pipelines, and performing hyperparameter tuning. As well as cross-validating alternative models to find the top performer

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30 Lessons (55m)

Show lesson playlist
  • 1. Liquidity Regressor Learning Objectives

    00:21
  • 2. Case Study Overview

    03:01
  • 3. Importing Data Workout

    01:09
  • 4. Metadata

    01:09
  • 5. Metadata Workout

    00:35
  • 6. Splitting Your Data

    01:57
  • 7. Splitting Your Data Workout

    02:04
  • 8. Train Test Split

    02:46
  • 9. Unpacking Lists

    01:51
  • 10. Unpacking Lists Workout

    01:24
  • 11. Progress Checkpoint

    00:13
  • 12. Model Pipelines

    04:38
  • 13. Model Pipelines Workout

    01:59
  • 14. Progress Checkpoint 1

    00:44
  • 15. Hyperparameter Tuning

    04:32
  • 16. Hyperparameter Grid Workout 1

    02:21
  • 17. Hyperparameter Grid Workout 2

    02:27
  • 18. Aggregating Hyperparameter Grids

    00:50
  • 19. Progress Checkpoint 2

    00:42
  • 20. Cross Validation

    02:06
  • 21. Creating Untrained Models

    02:13
  • 22. Creating Untrained Models Workout

    03:49
  • 23. Training and Tuning Models

    00:55
  • 24. Training and Tuning Models Workout

    02:29
  • 25. Model Evaluation

    02:02
  • 26. Model Evaluation Workout

    03:00
  • 27. Progress Checkpoint 3

    00:31
  • 28. Visualizing Model Predictions

    01:22
  • 29. Visualizing Model Predictions Workout

    01:04
  • 30. Using Your Model

    00:57

Prev: Regression Algorithms in Python Next: Classification Algorithms

Liquidity Regressor Learning Objectives

  • Notes
  • Questions
  • 00:21

What is covered in the playlist liquidity regressor.

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Machine Learning Python
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