Udemy - Credit Scoring with Machine Learning - A Practical Guide

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 1 - Welcome to the Course
• 1 -Course Introduction.mp4 (13.5 MB)
2 - Credit Scoring and Risk-Based Pricing
• 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)
3 - Introduction to Data Exploration and Analysis
• 1 -Introduction.mp4 (767.7 KB)
• 2 -Installing Jupyter Notebook Using Anaconda.mp4 (6.5 MB)
• 2 -httpswww.url (0.1 KB)
• 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 (0.1 KB)
• 4 -pandas.url (0.0 KB)
• 4 -seaborn.url (0.0 KB)
• 5 -Dataset Analysis.mp4 (22.0 MB)
• 5 -credit_scoring_dataset.csv (1.7 MB)
• 5 -demo_eda.ipynb (334.5 KB)
4 - Machine Learning in Credit Scoring
• 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 (0.1 KB)
• 13 -Random Forest.mp4 (50.5 MB)
• 13 -RandomForestClassifier.url (0.1 KB)
• 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 (0.1 KB)