Part 3 in the Fraud ML series
Part 2 in the Fraud ML series
Part 1 of the Fraud ML series
How to use Adversarial Validation to diagnose overfitting problems
Dealing with variation in experimental results
Exploring the vulnerabilities of ML models from attack by bad actors. They're more fragile than you might think
Some thoughts on how to apply version control when conducting experiments
Why git is critical for Data Science
Some important considerations for building and deploying models in industry
A beginner's guide to data science packages in Python