| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - SHAP and Shapley Values | |||
| 1 - SHAP motivation.mp4 | 20 MB | ||
| 10 - Permutation.mp4 | 82 MB | ||
| 11 - Permutation demo.mp4 | 77 MB | ||
| 12 - Sampling explainer.mp4 | 25.3 MB | ||
| 13 - SHAP kernel.mp4 | 57.7 MB | ||
| 14 - SHAP estimation for linear models.mp4 | 44.4 MB | ||
| 15 - SHAP for linear regression - demo.mp4 | 45.3 MB | ||
| 16 - SHAP for logistic regression.mp4 | 31.1 MB | ||
| 17 - SHAP for logistic regression - demo.mp4 | 50.6 MB | ||
| 18 - SHAP for decision tree-based models.mp4 | 45.8 MB | ||
| 19 - TreeSHAP - demo.mp4 | 43 MB | ||
| 2 - Congratulations! | |||
| 21 - Congrats and time to celebrate!.html | 6.1 KB | ||
| 22 - Bonus.html | 6.1 KB | ||
| 2 - Course materials.html | 6.5 KB | ||
| 20 - Additional reading resources.html | 7.1 KB | ||
| 3 - Game theory intuition.mp4 | 59.1 MB | ||
| 4 - Shaley value.mp4 | 63.1 MB | ||
| 5 - Shapley value calculation.mp4 | 84 MB | ||
| 6 - Shapley value calculation - demo.mp4 | 147.3 MB | ||
| 7 - Sampling values (Monte Carlo).mp4 | 63.7 MB | ||
| 8 - Exact explainer.mp4 | 49.3 MB | ||
| 9 - Exact explainer - demo.mp4 | 107.9 MB |
The Complete SHAP and Shapley Values Masterclass
https://WebToolTip.com
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 48m | Size: 1.07 GB
Understand SHAP values in depth to explain machine learning models accurately, critically and with confidence.
What you'll learn
Explain the intuition behind Shapley values and how they attribute model predictions
Calculate Shapley values correctly for machine learning models
Interpret local and global SHAP values, outputs, and visualizations
Use the SHAP Python library to explain model predictions
Evaluate SHAP’s assumptions and limitations and decide when it is appropriate
Requirements
Basic knowledge of Python and common machine learning workflows
Familiarity with model training, predictions, and feature importance
Some experience with pandas and scikit-learn
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