Machine learning (ML)

Machine learning involves giving a system a set of input and output data, which then transforms the data into a model (code), that is used to make intelligent decisions. This process compared to the Traditional Software development process which involves a person first finding patterns in the data and then writing code to convert the data to the desired outcome using the manual discovery patterns.

The Machine learning Process

The ML process involves the use of the Cross-Industry Standard Process for Data Mining (CRIS-DM), which is a step-by-step methodology for implementing a successful machine learning project. The ML process is made up of six steps, namely;

  1. Business understanding: This stage of the ML process involves identifying the problem, how to solve the problem, and whether machine learning will be a useful tool for solving it.

  2. Data understanding: This stage is where the available dataset is analysed and a decision is made whether to collect more data.

  3. Data preparation: At this stage of the ML process, the data is transformed into a tabular form which can be used as input for a machine learning model.

  4. Modelling: This stage of the ML process deals with training of models. In order words, where data is fed to an algorithm to discover patterns in the data.

  5. Evaluation: At this stage of the ML process, the performance of the model is evaluated to see if the model solves the original business problem.

  6. Deployment: The final stage of the ML process is where the model is deployed to production based on the results of the evaluation.