Udemy Credit Scoring with Machine Learning A Practical Guide
Seeders : 1 Leechers : 5
| Torrent Hash : | 9AF4D2F5B31E430726AA38D60523C4BD15E48720 |
| Torrent Added : | at July 31, 2025, 4:54 a.m. in Other |
| Torrent Size : | 926.5 MB |
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Udemy Credit Scoring with Machine Learning A Practical Guide
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Udemy Credit Scoring with Machine Learning A Practical Guide
Get Bonus Downloads Here.url -
1 -Course Introduction.mp4 -
1 -Introduction.mp4 -
2 -Loan Application Process.mp4 -
3 -Credit Score.mp4 -
4 -Credit Scoring.mp4 -
5 -Risk-Based Pricing.mp4 -
1 -Introduction.mp4 -
2 -Installing Jupyter Notebook Using Anaconda.mp4 -
2 -httpswww.url -
3 -Jupyter Notebook Interface.mp4 -
4 -Key Python Libraries for Data Analysis.mp4 -
4 -check_libraries.ipynb -
4 -matplotlib.url -
4 -pandas.url -
4 -seaborn.url -
5 -Dataset Analysis.mp4 -
5 -credit_scoring_dataset.csv -
5 -demo_eda.ipynb -
1 -Introduction.mp4 -
10 -Logistic Regression Classifier.mp4 -
11 -Balancing False Positives and False Negatives.mp4 -
12 -Logistic Regression Classifier – demo.mp4 -
12 -credit_scoring_dataset.csv -
12 -logistic_regression.ipynb -
13 -DecisionTreeClassifier.url -
13 -Random Forest.mp4 -
13 -RandomForestClassifier.url -
13 -decision_tree_visualization.ipynb -
14 -Decision Tree Structure.mp4 -
14 -decision_tree_visualization.ipynb -
15 -Random Forest – demo.mp4 -
15 -random_forest.ipynb -
16 -Scikit-learn Pipeline.mp4 -
17 -Scikit-learn Pipeline – demo.mp4 -
17 -random_forest_pipeline.ipynb -
18 -Saving and Loading Machine Learning Models for Predictions.mp4 -
19 -Predictions with Random Forest Pipeline – demo.mp4 -
19 -random_forest_pipeline_predictions.ipynb -
2 -Exploring the Credit Scoring Dataset.mp4 -
2 -essential_features_for_effective_credit_scoring.ipynb -
20 -k-fold cross-validation.mp4 -
21 -k-fold cross-validation – demo.mp4 -
21 -random_forest_kfold.ipynb -
22 -ROC, AUC, and Cost-Based Metrics.mp4 -
22 -auc_and_roc_curve.ipynb -
23 -Divergence Analysis.mp4 -
23 -divergence_analysis.ipynb -
24 -Risk-Based Grouping.mp4 -
24 -data.joblib -
24 -rf_model.joblib -
24 -risk_based_grouping.ipynb -
25 -Wrapping Up Key Takeaways and Next Steps.mp4 -
3 -Types of Machine Learning.mp4 -
4 -Machine Learning Workflow Overview.mp4 -
5 -Introduction to Scikit-Learn.mp4 -
6 -Confusion Matrix.mp4 -
7 -Implications of False Positives in Credit Scoring.mp4 -
8 -Implications of False Negatives in Credit Scoring.mp4 -
9 -Performance Metrics.mp4 -
Bonus Resources.txt -
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Get Bonus Downloads Here.url -
180 bytes
1 -Course Introduction.mp4 -
13.5 MB
1 -Introduction.mp4 -
7.4 MB
2 -Loan Application Process.mp4 -
5.4 MB
3 -Credit Score.mp4 -
10.3 MB
4 -Credit Scoring.mp4 -
4.6 MB
5 -Risk-Based Pricing.mp4 -
8.5 MB
1 -Introduction.mp4 -
767.7 KB
2 -Installing Jupyter Notebook Using Anaconda.mp4 -
6.5 MB
2 -httpswww.url -
56 bytes
3 -Jupyter Notebook Interface.mp4 -
35.7 MB
4 -Key Python Libraries for Data Analysis.mp4 -
25.6 MB
4 -check_libraries.ipynb -
57.2 KB
4 -matplotlib.url -
74 bytes
4 -pandas.url -
48 bytes
4 -seaborn.url -
49 bytes
5 -Dataset Analysis.mp4 -
22.0 MB
5 -credit_scoring_dataset.csv -
1.7 MB
5 -demo_eda.ipynb -
334.5 KB
1 -Introduction.mp4 -
714.6 KB
10 -Logistic Regression Classifier.mp4 -
24.2 MB
11 -Balancing False Positives and False Negatives.mp4 -
7.7 MB
12 -Logistic Regression Classifier – demo.mp4 -
78.6 MB
12 -credit_scoring_dataset.csv -
1.7 MB
12 -logistic_regression.ipynb -
87.6 KB
13 -DecisionTreeClassifier.url -
113 bytes
13 -Random Forest.mp4 -
50.5 MB
13 -RandomForestClassifier.url -
117 bytes
13 -decision_tree_visualization.ipynb -
687.9 KB
14 -Decision Tree Structure.mp4 -
12.1 MB
14 -decision_tree_visualization.ipynb -
687.9 KB
15 -Random Forest – demo.mp4 -
62.4 MB
15 -random_forest.ipynb -
129.2 KB
16 -Scikit-learn Pipeline.mp4 -
6.5 MB
17 -Scikit-learn Pipeline – demo.mp4 -
89.7 MB
17 -random_forest_pipeline.ipynb -
118.8 KB
18 -Saving and Loading Machine Learning Models for Predictions.mp4 -
12.3 MB
19 -Predictions with Random Forest Pipeline – demo.mp4 -
10.9 MB
19 -random_forest_pipeline_predictions.ipynb -
8.7 KB
2 -Exploring the Credit Scoring Dataset.mp4 -
9.0 MB
2 -essential_features_for_effective_credit_scoring.ipynb -
20.0 KB
20 -k-fold cross-validation.mp4 -
28.8 MB
21 -k-fold cross-validation – demo.mp4 -
70.7 MB
21 -random_forest_kfold.ipynb -
8.7 KB
22 -ROC, AUC, and Cost-Based Metrics.mp4 -
33.4 MB
22 -auc_and_roc_curve.ipynb -
65.0 KB
23 -Divergence Analysis.mp4 -
24.5 MB
23 -divergence_analysis.ipynb -
158.8 KB
24 -Risk-Based Grouping.mp4 -
92.4 MB
24 -data.joblib -
969.0 KB
24 -rf_model.joblib -
27.5 MB
24 -risk_based_grouping.ipynb -
168.2 KB
25 -Wrapping Up Key Takeaways and Next Steps.mp4 -
14.6 MB
3 -Types of Machine Learning.mp4 -
18.0 MB
4 -Machine Learning Workflow Overview.mp4 -
20.3 MB
5 -Introduction to Scikit-Learn.mp4 -
39.6 MB
6 -Confusion Matrix.mp4 -
9.1 MB
7 -Implications of False Positives in Credit Scoring.mp4 -
10.6 MB
8 -Implications of False Negatives in Credit Scoring.mp4 -
9.7 MB
9 -Performance Metrics.mp4 -
15.7 MB
Bonus Resources.txt -
70 bytes
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