Master Credit Risk Modeling with Python - Pro 2024
Focused View
5:59:05
1 -Course Overview.mp4
03:02
2 -Setting Up Your Computer.mp4
00:59
3 -Overview of Credit Risk Models.mp4
09:18
4 -Applications in the Industry.mp4
01:10
1 -final project solution.zip
1 - Documents.html
1 - Python codes.html
1 -Introduction to Probability of Default (PD) Models.mp4
02:17
2 -Example Case Presentation.mp4
03:52
3 -Application vs Behavioral Scorecards.mp4
05:06
1 -Dataset Information.mp4
04:34
2 -Loading data to the Python environment.mp4
02:30
1 -Data Quality Checks.mp4
07:40
2 -Data Cleaning.mp4
07:38
3 -Exploratory Data Analysis.mp4
05:30
4 -Exploratory Data Analysis - Based on Time.mp4
05:42
5 -Sector Best Practices.mp4
02:59
1 -Data Transformation Methods.mp4
04:00
2 -Data Transformation in Practice.mp4
04:55
3 -Sector Best Practices.mp4
08:56
1 -Data Splitting Methods.mp4
04:28
2 -Data Splitting In Practice.mp4
04:38
1 -Overview and Sector Best Practices.mp4
05:29
2 -Correlation Elimination.mp4
03:45
3 -Correlation Elimination In Practice.mp4
03:50
4 -Information Value.mp4
01:21
5 -Information Value in Practice.mp4
01:44
6 -Univariate Gini.mp4
04:28
7 -Univariate Gini In Practice.mp4
04:27
1 -Survival Analysis.mp4
05:11
2 -Survival Analysis In Practice.mp4
04:45
3 -Logistic Regression.mp4
03:25
4 -Logistic Regression In Practice.mp4
05:00
5 -Logistic Regression Model Explainability Methods.mp4
01:30
6 -Logistic Regression Model Explainability Methods In Practice.mp4
01:30
7 -Model Coefficients.mp4
02:55
8 -Logistic Regression - Max Gini Model.mp4
03:50
9 -Logistic Regression - Max Gini Model Predictions.mp4
02:13
10 -K Fold Cross Validation.mp4
04:08
11 -K Fold Cross Validation In Practice.mp4
04:35
12 -Sector Best Practices.mp4
15:56
1 -Advanced Feature Importance Overview.mp4
08:19
2 -Random Forest Feature Selection.mp4
03:05
3 -Shapley Values Feature Selection.mp4
03:10
4 -Permutation Feature Importance Selection.mp4
02:35
1 -XGBoost Overview.mp4
09:58
2 -XGBoost.mp4
04:27
3 -Approximate Coefficients for XGBoost.mp4
02:45
4 -Parameter Tuning for XGBoost.mp4
02:05
5 -Neural Networks Overview.mp4
12:08
6 -Neural Networks.mp4
02:28
7 -Parameter Tuning for Neural Networks.mp4
03:37
8 -Model Ensembling.mp4
03:29
9 -Model Ensembling In Practice.mp4
02:40
10 -Sector Best Practices.mp4
02:26
1 -Model Selection Methodology.mp4
03:03
2 -Model Selection In Practice.mp4
01:54
1 -Rating Scale Overview.mp4
03:32
2 -Rating Scale Generation.mp4
03:32
3 -Score Generation and Scaling.mp4
03:42
4 -Sector Best Practices.mp4
05:00
1 -Why Model Calibration Needed.mp4
05:39
2 -Bayesian Calibration.mp4
04:03
3 -Regression Calibration.mp4
04:26
4 -Sector Best Practices.mp4
02:25
1 -Model Validation Basics and Sector Best Practices.mp4
06:12
2 -Validation Metrics for Credit Scoring Models.mp4
31:25
3 -AUC ROC.mp4
02:35
4 -Time Series Gini.mp4
03:21
5 -Kolmogorov-Smirnov Test.mp4
02:53
6 -Confusion Matrix.mp4
03:25
7 -Stability Tests - PSI & SSI.mp4
03:00
8 -Variance Inflation Factor.mp4
03:30
9 -Herfindahl-Hirshman Index and Adjusted Herfindahl-Hirshman Index.mp4
02:59
10 -Anchor Point.mp4
02:57
11 -Chi-Square Test.mp4
03:03
12 -Binomial Test.mp4
03:09
13 -Adjusted Binomial Test.mp4
03:13
14 -Model Validation Thresholds.mp4
04:15
1 -Case Study 1 - U.S. based Financing Company.mp4
05:27
2 -Case Study 2 - UK based Fintech Startup.mp4
05:25
1 -Final Project Using Real-World Data.mp4
02:32
More details
Course Overview
This comprehensive course teaches you to build advanced credit risk models using Python, covering everything from data preprocessing to model validation with real-world datasets and industry best practices.
What You'll Learn
- Construct complete credit risk models using Python
- Apply advanced techniques like XGBoost and Neural Networks
- Evaluate models using industry-standard validation metrics
Who This Is For
- Banking and finance professionals
- Aspiring credit risk analysts
- Data scientists in financial services
Key Benefits
- Hands-on experience with real-world datasets
- Learn sector best practices from global experts
- Master both classical and advanced modeling techniques
Curriculum Highlights
- Fundamentals of Credit Risk Scoring
- Advanced Data Science Techniques
- Model Evaluation & Validation
Focused display
Category
- language english
- Training sessions 79
- duration 5:59:05
- Release Date 2025/06/10
