Introduction to Supervised, Unsupervised, and Reinforcement Learning

Machine learning (ML), a subset of artificial intelligence (AI), focuses on enabling machines to learn from data and make decisions or predictions without being explicitly programmed. As data continues to grow exponentially in both volume and complexity, machine learning has become essential across a wide range of applications, from autonomous vehicles to financial modeling. Among the various types of machine learning techniques, supervised learning, unsupervised learning, and reinforcement learning are Supervised learning is perhaps the most commonly used approach in machine learning today. It involves training a model on a labeled dataset, where each input data point is paired with the correct output or target label. The model learns to map inputs to outputs by identifying patterns and relationships in the data. Once trained, the model can then predict the output for new, unseen inputs with a reasonable level of accuracy.