Best Practices

Noisy labels can wreak havoc

Part 3 in the Fraud ML series

Challenges of ML with PII data

Part 2 in the Fraud ML series

How to build better fraud detection systems with ML

Part 1 of the Fraud ML series

Adversarial Validation

How to use Adversarial Validation to diagnose overfitting problems

Signal : Noise

Dealing with variation in experimental results

Machine Learning Security

Exploring the vulnerabilities of ML models from attack by bad actors. They're more fragile than you might think

Implementing Git in Data Science

Some thoughts on how to apply version control when conducting experiments

Committing to Sanity

Why git is critical for Data Science

More than Algorithms

Some important considerations for building and deploying models in industry

Python for data analysis

A beginner's guide to data science packages in Python