Within AI, one of the most significant branches is machine learning, which focuses on training algorithms to recognize patterns, make predictions, or assist in decision-making by analyzing data. Instead of being explicitly coded for every task, these models learn from examples and adapt to new information.

Machine learning covers a wide range of methods and algorithms. Common examples include linear and logistic regression, decision trees, random forests, support vector machines (SVMs), k-nearest neighbors (KNN), clustering techniques, and many others. Each algorithm is designed to address specific types of problems depending on the structure and complexity of the data.

Among these approaches, neural networks have gained particular popularity. Inspired by the structure of the human brain, neural networks are made up of layers of connected nodes (similar to biological neurons) that work collaboratively to interpret and process information. This design makes them especially effective at uncovering hidden patterns and relationships within massive and complex datasets.

One of the foundational methods in this field is supervised learning. In this approach, algorithms are trained on datasets that include both input data and their correct labels. By learning the connection between inputs and outputs, the model becomes capable of predicting outcomes or classifying new, previously unseen data with accuracy.

Source: https://www.ibm.com/think/topics/artificial-intelligence