Machine Learning Algorithms

There are four main types of learning algorithms:

• Supervised learning algorithm: This is trained to predict an outcome for a given set of input features. It's well studied and widely used in many areas such as spam classification, fraud detection, and product recommendation.
• Unsupervised learning algorithm: This analyzes the underlying patterns or structure of data and groups data into clusters. Examples are outlier detection, fraud detection, and dimensionality reduction.
• Semi-supervised learning: This falls between supervised learning and unsupervised learning. It's intended to boost learning accuracy for a supervised learning model by mixing unlabeled data.
• Reinforcement learning algorithm: This is trained to play a "game." It learns to take a "smarter" action at each step in a game so that it will eventually win the game. Examples are AlphaGo, robot control, and quantitative trading.

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