The general approach to solving machine learning consists of a series of phases. These phases are consistent no matter he source of data. That is, be it structured or unstructured, the stages required to tackle any kind of data are as shown in the following diagram:

We will discuss each of the phases in detail as follows:

• The analysis phase: In this phase, the ingested data is analyzed to detect patterns in the data that help create explicit features or parameters that can be used to train the model.
• The training phase: Data parameters generated in the previous phases are used to create machine learning models in this phase. The training phase is an iterative process, where the data incrementally helps to improve the quality of prediction.
• The testing phase: Machine learning models created in the training phase are tested with more data and the model's performance is assessed. In this stage we test with data that has not been used in previous phase. Model evaluation at this phase may or may not require parameter training.
• The application phase: The tuned models are finally fed with real-world data at this phase. At this stage, the model is deployed in the production environment.