Machine learning is concerned with developing techniques that allow the applications to learn without having to be explicitly programmed to solve a problem. This flexibility allows such applications to be used in more varied settings with little to no modifications.

We saw how training data is used to create a model. Once the model has been trained, the model is evaluated using testing data. Both the training data and testing data come from the problem domain. Once trained, the model is used with other input data to make predictions.

We learned how the Weka Java API is used to create decision trees. This tree consists of internal nodes that represent different attributes of the problem. The leaves of the tree represent results. Since there are many ways of constructing a tree, part of the job of a decision tree is to create the best tree.

Support vector machines divide a dataset into sections thus classifying elements in the dataset. This classification s based on the attributes of the data such as age, hair color, or weight. With the model, it is possible to predict outcomes based on attributes of a data instance.

Bayesian networks are used to make predictions based on parent-child relationships between nodes. The probability of one event directly affects the probability of a child event, and we can use this information to predict outcomes of complex real-world environments.

In the section on association rule learning, we learned how the relationships between elements of a dataset can be identified. The more significant relationships allow us to create rules that are applied to solve various problems.

In our discussion of reinforcement learning, we discussed the elements of agent, state, action, and reward and their relationship to one another. We also discussed specific types of reinforcement learning and provided resources for further inquiry.

Having introduced the elements of machine learning, we are now ready to explore neural networks, found in the next chapter.