Loss, gradient descent, classic algorithms, evaluation, and working with data and features.
New to machine learning and comfortable with basic math. 4 paths · 27 explainers.
Prediction tasks, loss functions, gradient descent, and the knobs that drive training
Trees, SVMs, clustering, and ensemble methods beyond neural nets
Validation, cross-validation, the bias–variance tradeoff, and pitfalls
Cleaning data, building features, embeddings, transfer learning, and dimensionality reduction