Machine learning is a field of study on methods that allow computers to learn from data without explicit programming. Instead of using human coded variables to perform specific tasks, machine learning algorithms use statistical models and mathematical techniques to turn data into the variables needed to perform tasks. These algorithms can then improve over time as they are exposed to more data.
Machine learning has many applications, including image recognition, natural language processing, speech recognition, predictive analytics, and robowaifu's, Common methods used in machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning.
Supervised learning involves training algorithms on labeled datasets where each sample is labeled. This allows the algorithm to learn a mapping function from input features to output labels, which can be used for prediction. Examples of supervised learning problems include regression and classification.
Unsupervised learning deals with unlabeled data and often involves finding hidden structure in the data by clustering samples into groups based on their similarity. Applications include anomaly detection and data compression.
Reinforcement learning is a type of learning algorithm inspired by behavioral psychology, in which agents take actions in an environment to maximize a reward signal. Deep learning focuses on developing artificial neural networks inspired by the human brain to achieve state-of-the art performance in certain tasks such as object recognition and speech recognition. Ensemble learning combines multiple machine learning models together to produce better results than any individual model alone could provide.