Describe Fundamental Principles of Machine Learning on Azure
Learning types
Supervised learning trains on labelled data - each row has features (inputs) and a label (the answer). Two flavours: regression predicts a numeric value (price, units, temperature); classification predicts a category (yes/no, or one of several classes). Unsupervised learning has no label; clustering groups similar rows. Reinforcement learning learns by reward and penalty through trial and error. A quick grep of the scenario is usually enough: continuous answer = regression, discrete answer = classification, no answer column = clustering.
The Azure Machine Learning toolset
An Azure Machine Learning workspace organises experiments, data, compute and models. Automated ML sweeps algorithms and settings for you and exposes a leaderboard of models with their metrics. The designer gives a drag-and-drop pipeline canvas. Pipelines automate repeatable steps; compute clusters scale training; a model registry versions artefacts; an endpoint serves a deployed model.
Evaluation and traps
Split data into training, validation and test sets - never evaluate on data the model trained on, which is overfitting plus data leakage. Regression metrics: MAE, RMSE, R² (higher R² is better). Classification metrics: accuracy, precision, recall, F1, AUC. Classic traps: choosing classification for a numeric target, treating clustering as supervised, and assuming accuracy is a good metric on imbalanced data (prefer precision/recall/AUC).
Worked example
Predicting "how many bikes will be rented tomorrow" needs a numeric label, so it is regression - even though the count is a whole number. Predicting "will this customer churn" is binary classification. Grouping customers with no churn label is clustering.