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Machine learning is categorized into three main types, each addressing different challenges and serving distinct purposes. Understanding these types is crucial for choosing the most suitable approach for specific tasks.

1. Unsupervised learning

The unsupervised learning category deals specifically with unlabeled data. In this type of learning, the model learns patterns and relations within the data without any explicit guidance. Unsupervised learning algorithms explore data structures to determine groupings and inherent patterns. This type of learning is commonly used for anomaly detection and clustering.

• Anomaly detection— It identifies abnormal or rare instances in data. It is mainly used to monitor unusual events, detect faults, and detect fraud.
• Clustering—The similarity of this group's data points depends on their similarity. It's useful in data exploration, pattern recognition, and customer segmentation.

2. Supervised learning

Supervise the learning process, including data labelling, to train the ML models. The ML models learn from input-output pairs to make accurate predictions on unseen data. Regression and classification are common applications of this learning.

• Regression— It predicts a consistent output depending on the input data. For instance, the prediction of house prices depends upon features like the number of rooms, location, and size.
• Classification—This model assigns the input data to predefined classes or categories, such as spam or primary emails.

3. Reinforcement learning

Reinforcement learning involves the agent learning through interaction with the environment and receiving feedback in the form of penalties or rewards. It is commonly applied in situations where actions have an impact on subsequent data.