Machine Learning

The first post in a beginner-friendly series on machine learning in finance, built around the problems banks actually run models on: credit scoring, fraud detection, and anti-money-laundering analytics, with market prediction as a supporting track. No code yet. Instead, the things I wish every newcomer knew before training a model on financial data: why it breaks the textbook assumptions, the vocabulary in every model notebook, the mistakes that make models look brilliant in development and fail in production, and a reference table of the metrics that matter, with which direction is good for each.