Ready to BOOST your hands-on MACHINE LEARNING skills in PYTHON?

COVID-19 has created many challenges for credit risk analytics. Join our community of analysts who master Machine Learning in Python and R using real time credit data and thousands of code lines. What are you waiting for?

"Deep Credit Risk — Machine Learning in Python and R" aims at starters and pros alike to enable you to:

• Understand the role of liquidity, equity and many other key banking features;
• Engineer and select features;
• Predict defaults, payoffs, loss rates and exposures;
• Predict downturn and crisis outcomes using pre-crisis features;
• Understand the implications of COVID-19;
• Apply innovative sampling techniques for model training and validation;
• Deep learn from Logit Classifiers to Random Forests and Neural Networks;
• Do unsupervised Clustering, Principal Components and Bayesian Techniques;
• Build multi-period models for CECL, IFRS 9 and CCAR;
• Build credit portfolio correlation models for value-at-risk and expected shortfall;
  
• Detailed instructions and materials for new learners;
  
• Run over 1,500 lines of code; and
• Access real credit data and much more …

Hands-on Machine Learning programming in PYTHON

Contents

Get Python Edition

Get R Edition

购书

Python Examples

"It is bound to be of benefit to you whether you are a beginner
or a seasoned credit risk modeler."

- Anyruddho Sanyal, Owner, Phoenix Computing Solutions

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Start Python

Start R