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Machine Learning is a branch of data science and has become one of the essential research topics in information technology. In daily life it plays an increasing role in people's lives. For example, robust email spam filters, speech and text recognition software, web search engines, etc. We can also add the advancement in the construction of safe and efficient autonomous cars that are already beginning to be a close reality, in addition to notable progress in medical applications, such as the detection of skin cancer through the analysis of images.

It is a process that makes it possible to develop computer systems that learn without having to make this skill explicit in the program code. The main objective is to create models that understand data and find underlying patterns. We can define it as a process by which a computer can operate more accurately as it collects and learns from the data provided.

Although we can see Machine Learning as a branch of Artificial Intelligence, the main focus of it is to improve the performance of a system, and the experience built does not always have to be equal to human behavior. On the other hand, employing algorithms that behave like human intelligence is one of the main focuses of artificial intelligence.

Machine Learning has a close relationship with several other fields, including in addition to Artificial Intelligence, Data Mining, Statistics, Data Science, among others. Therefore, Machine Learning is a multidisciplinary field and, in a way, is linked to all of these fields mentioned.

Machine Learning Models

Machine Learning models are based on data structures that create associations, establish relationships, associate patterns that generate new samples of data. This process produces well-defined data sets related to a specific context, such as the temperature of a room sampled in each defined period or the heights of a population of individuals. In many situations, the conditions defined for the learning models are not consistent, which can lead to a long training process that does not always result in reliable validation.

Building Machine Learning Models

  • Data pre-processing
    The technique consists in converting in a representative and consistent way the data collected normally in an uncontrolled way and that for this reason usually contain discrepancies.
  • Model Learning
    The data is divided into training and testing sets, where the sets of methods or algorithms that will be used for learning and which patterns can be used later to make predictions are defined.
  • Model evaluation
    Using metrics to evaluate your performance, adjustments are made to the defined parameters, optimizing your objectives.
  • Prediction
    The model is submitted to forecasts, exposing it to a new set of data. These forecasts should normally be shared with users of the system to choose the most effective options.
  • Implementation of the model
    At this stage, the model needs adequate management and maintenance at regular intervals to keep it up and running.

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