Machine Learning
Machine learning turns data into predictions, rankings, recommendations, and automated decisions.
supervised & unsupervised
bias-variance
train/test/validation
evaluation metrics
regularization
ensembles
feature engineering
drift & monitoring
Topic overview
Learn supervised and unsupervised learning, feature engineering, validation, metrics, regularization, and the deployment concerns that keep a model working after launch.
Core concepts
Bias-variance tradeoff, data leakage, train/test/validation splits, cross-validation, precision/recall/calibration, L1/L2 regularization, bagging vs boosting, feature encoding and selection, and data/concept drift.
Why it matters
Useful ML requires more than algorithms: it needs correct problem framing, honest evaluation, disciplined data handling, and a plan for what happens after the model ships.
Interview relevance
Expect questions about choosing the right model and metric, diagnosing overfitting vs underfitting, spotting leakage, and explaining tradeoffs to a non-ML stakeholder.