Machine Learning Algorithm families
Supervised Learning (e.g. classification, anomaly detection, regression)
- Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
- A model is prepared through a training process where it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
- Example problems are classification and regression.
- Example algorithms include Logistic Regression and the Back Propagation Neural Network.
Unsupervised Learning (e.g. clustering and dimensionality reduction)
- Input data is not labelled and does not have a known result.