# Getting Started

At the high-level machine learning algorithms can be categorized into 2 different categories based on their learning style.<br>

1. **Supervised Learning**

Input data is called training data and has a known label or result such as spam/not-spam(classifier) or a stock price(numerical value). A model is prepared through a training process in which 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.

2. **Unsupervised Learning**

Input data is not labeled and does not have a known result. A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems are clustering, conditionality reduction, and association rule learning. Example algorithms include the Apriori algorithm and K-Means.

<https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/>


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