| 1 -Course Introduction.mp4 | 13.5 MB | ||
| 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 | 102.4 B | ||
| 13 -Random Forest.mp4 | 50.5 MB | ||
| 13 -RandomForestClassifier.url | 102.4 B | ||
| 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 MB | ||
| 2 -Installing Jupyter Notebook Using Anaconda.mp4 | 6.5 MB | ||
| 2 -Loan Application Process.mp4 | 5.4 MB | ||
| 2 -essential_features_for_effective_credit_scoring.ipynb | 20 KB | ||
| 2 -httpswww.url | 102.4 B | ||
| 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 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 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 -Credit Score.mp4 | 10.3 MB | ||
| 3 -Jupyter Notebook Interface.mp4 | 35.7 MB | ||
| 3 -Types of Machine Learning.mp4 | 18 MB | ||
| 4 -Credit Scoring.mp4 | 4.6 MB | ||
| 4 -Key Python Libraries for Data Analysis.mp4 | 25.6 MB | ||
| 4 -Machine Learning Workflow Overview.mp4 | 20.3 MB | ||
| 4 -check_libraries.ipynb | 57.2 KB | ||
| 4 -matplotlib.url | 102.4 B | ||
| 4 -pandas.url | 0 B | ||
| 4 -seaborn.url | 0 B | ||
| 5 -Dataset Analysis.mp4 | 22 MB | ||
| 5 -Introduction to Scikit-Learn.mp4 | 39.6 MB | ||
| 5 -Risk-Based Pricing.mp4 | 8.5 MB | ||
| 5 -credit_scoring_dataset.csv | 1.7 MB | ||
| 5 -demo_eda.ipynb | 334.5 KB | ||
| 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 | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 61 total files | |||
Credit Scoring with Machine Learning: A Practical Guide
https://WebToolTip.com
Published 7/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 27m | Size: 927 MB
Learn Credit Scoring, Machine Learning, and Python
What you'll learn
Develop a solid understanding of credit scoring and risk-based pricing, and how these concepts are used in real-world lending decisions
Build, train, and evaluate machine learning models using Scikit-learn and Python
Explore and prepare credit data using pandas and Jupyter Notebook
Interpret model outputs and performance metrics, including confusion matrices, ROC curves, AUC, and cost-based evaluation
Understand the impact of false positives and false negatives, and how to balance them in credit scoring use cases
Apply cross-validation techniques, divergence analysis, and risk-based grouping
Use Scikit-learn Pipelines to streamline preprocessing and ensure reproducible, production-ready workflows
Translate technical results into business insights, empowering data-driven decision-making in credit risk and beyond
Requirements
Basic knowledge of data analysis concepts
Basic knowledge of Python (helpful but not required)
No prior experience with credit scoring or machine learning needed
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1009.3 MB | freecoursewb | 5 months | 2 | 6 | |
| 3.1 GB | freecoursewb | 8 months | 0 | 0 | |
| 816.9 MB | freecoursewb | 10 months | 0 | 0 | |
| 2 GB | freecoursewb | 1 year | 0 | 0 | |
| 1.6 GB | freecoursewb | 1 year | 6 | 5 |
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