Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that involves training machines to learn from data and make predictions or decisions without being explicitly programmed. In other words, instead of writing explicit rules for the machine to follow, we provide the machine with data and let it learn through experience. ML is often used to solve problems that are too complex to be solved with traditional programming methods.
Traditional programming involves writing code that explicitly tells a computer what to do. For example, if we wanted to write a program to detect spam emails, we would need to write specific rules to identify spam, such as looking for certain keywords, analyzing the sender’s email address, etc. However, these rules may not be comprehensive enough or may not account for all possible scenarios.
On the other hand, in ML, we provide the machine with a dataset of emails that are labeled as either spam or not spam. The machine then learns from this data using various algorithms and statistical models to identify patterns and relationships that can be used to classify new emails as either spam or not spam. The machine is not explicitly told what to look for, but it learns from the data and identifies the relevant features itself.
ML algorithms can be broadly divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the machine is trained on labeled data, and the goal is to predict the output for new, unseen data based on the input features. In unsupervised learning, the machine is trained on unlabeled data, and the goal is to identify patterns and relationships within the data. In reinforcement learning, the machine learns by trial and error to achieve a specific goal through a system of rewards and punishments.
In summary, ML differs from traditional programming in that it involves providing machines with data and allowing them to learn from experience rather than being explicitly programmed. This approach allows machines to solve complex problems that may be difficult or impossible to solve with traditional programming methods.