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 using real time credit data and thousands of code lines. What are you waiting for?

"Deep Credit Risk — Machine Learning in Python" 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;
• Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code; and
• Access real credit data and much more …

Hands-on Machine Learning programming in PYTHON

Contents

Examples

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"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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