Training

Machine learning is computational methods that learn from experience to improve performance or to make accurate predictions. Some of the more popular machine learning algorithms include regression, decision trees, random forest, artificial neural networks, and support vector machines. These models are trained on existing data by running large amounts of data through the model until it finds enough patterns to be able to make accurate decisions about that data. The trained model is then used to score new data to make predictions. Some applications for these models include churn prediction, sentiment analysis, recommendations, fraud detection, online advertising, pattern and image recognition, prediction of equipment failures, web search results, spam filtering, and network intrusion detection. There are a number of learning scenarios, or types of learning algorithms, that can be used depending on whether a target variable is available and how much labelled data can be used. These approaches include supervised, unsupervised, and semi-supervised learning; reinforcement learning is an approach often used in robotics but also used in several recent machine learning breakthroughs.