Machine learning is an area of computer science and statistical modeling that allows a computer program to predict an outcome or make a decision without being explicitly programmed to do so.
Machine learning, which forms the basis for artificial intelligence (AI), is closely tied to data analytics and data mining programming. Both machine learning and data mining applications use mathematical algorithms to search through data and look for patterns. However, instead of extracting data for human comprehension, as is the case in data mining applications, machine learning uses algorithms to detect patterns in data and adjust program actions accordingly.
Big data and cloud-based predictive analytics services are helping programmers and data scientists to take advantage of machine learning in new ways. For example, Facebook’s News Feed changes according to the user’s personal interactions with other users. If a user frequently tags a friend in photos, writes on his wall or “likes” his links, the user’s News Feed will show more of that friend’s activity due to presumed closeness.
Machine learning tasks are typically classified into three broad categories: supervised learning, unsupervised learning and reinforcement learning. In supervised learning, the computer uses input examples and their desired outputs to determine a general rule for mapping inputs to outputs. Spam filters use supervised learning. In unsupervised learning, the computer is given input examples and is asked to cluster things that are alike. Google news, which clusters random news articles into topics, uses unsupervised machine learning. In reinforcement learning, the computer gathers inputs from its surroundings to accomplish a task. Self-driving cars use reinforcement learning.