computers learn and make predictions from data. At its core, machine learning focuses on the development of algorithms that enable computers to learn patterns and make decisions without being explicitly programmed. This approach allows machines to improve their performance over time as they are exposed to more data, making them increasingly adept at handling complex tasks.
One of the key concepts in machine learning is the idea of training a model. During the training process, a model is fed with a large amount of data that contains input-output pairs. The model learns the underlying patterns in the data and adjusts its internal parameters to minimize the difference between its predictions and the actual outputs. This process involves various techniques such as linear regression, decision trees, support vector machines, and neural networks.
