## Supervised Machine Learning

Take the example of college admissions. For simplicity, let us assume that the admission board makes a decision based on your school grade, SAT score, and interview outcome. Given this dataset, the algorithm learns and tunes the model to achieve a desired level of accuracy.

Supervised learning problems are broadly classified into 2 categories:

Supervised learning problems are broadly classified into 2 categories:

- Regression - The model tries to predict results with a continuous output.
- Categorization - The model tries to predict results in a discrete set of outputs.

## Unsupervised Machine Learning

Think of building the decision control on an unmanned vehicle going to an alien planet. To start with there is insufficient or no data available to train. Secondly the factors that influence the outcome are not yet determined. In such a case you need an algorithm that can take in the increasing data sources, be able to identify the influence and magnitude of influence of each data source and also be able to revise it's learning as the data sets grow. Visually this could be a example of generating a cluster graph of decisions.

## Reinforcement Machine Learning

This can be seen as a step further than unsupervised learning where the algorithm receives a reward or penalty for the outcomes it generates. Based on this feedback, it decides on the next steps for tuning it's model.

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