Introduction to ML

The Machine Learning (ML), or machine learning, is a branch of artificial intelligence that allows computer systems to learn from data and make predictions or decisions without being explicitly programmed for each case. Instead of following fixed rules, machine learning models analyze large amounts of information to detect patterns and repetitive behaviors. Its main advantage is that it automates complex analysis processes and improves over time as it receives more data. This capability is especially useful in environments where there is a large volume of information and a quick and accurate response is required.

There are three main types of machine learning, each adapted to different needs:

• Supervised learning: The model learns from a set of labeled data, that is, where the correct answer is known. For example, a model can learn to predict the energy consumption of a building (numerical value: regression) or classify whether a device is in a normal state or failing (category: classification). It is the most used in industrial and business applications.

• Unsupervised learning: It is used when there are no prior labels. The goal is to discover natural structures or patterns in the data, such as grouping customers with similar behaviors (clustering). This type of learning is useful for segmentation, exploratory analysis, and anomaly detection.

• Reinforcement learning (less common in standard industrial environments): The system learns through trial and error, receiving rewards or penalties. It is common in robotics or advanced automation.