Machine learning
WHAT IS IT?
Machine learning is a field within artificial intelligence that focuses on developing algorithms capable of learning from and making predictions or decisions based on data. This enables machines to carry out tasks without being explicitly programmed for every step. The core of machine learning lies in its ability to generalize from past data to unseen, future data, enhancing its performance in tasks such as natural language processing, computer vision, and predictive analytics without direct human intervention.
Machine learning models can be categorized into supervised learning, where models learn from labeled data; unsupervised learning, which identifies patterns in unlabeled data; and reinforcement learning, where models learn through trial and error and are rewarded for correct decisions. These models rely on various algorithms, including neural networks for pattern recognition and decision trees for data classification and prediction.
Machine learning’s application ranges widely, from customer service enhancements with chatbots to advanced fraud detection and the learning of process times. It represents a significant shift in how data-driven decisions and tasks are approached, offering descriptive, predictive, and prescriptive functions based on vast amounts of data. Moreover, through machine learning, decision intelligence applications can be supplemented by feedback loops, or closed-loop learning to continuously refine and enhance the system. By comparing the AI‘s output (such as predictions or recommendations) with the ultimate decision made (for instance, whether to proceed with a certain action), it provides feedback that enables the system to learn and enhance the accuracy of future recommendations.
