Machine learning estimates a mapping from observed data to a prediction or decision. The hard part is defining the target, split, metric, and deployment conditions so the reported score means what the application needs.
Choose a starting point
Start with data and baselines, then compare supervised methods, validation designs, feature work, and error analysis. Keep a held-out evaluation tied to the future population and use projects to test the whole pipeline.
- Machine Learning Tutorial
- Machine learning starts with a baseline and a valid prediction clock
- Prediction-time feature availability: reject future information before training
- Regression baselines and honest holdout metrics
- Hyperparameter search and an untouched final test
- Bootstrap bagging and out-of-bag evaluation
- Logistic regression, log loss and odds
- Nearest-neighbor distance and local support
- Pseudo-label selection and contamination control
Common Mistakes
Leakage, shifted labels, and a mismatched metric can make a high score useless. The linked lessons examine those failures alongside model families and tuning methods.