Description
Supervised Learning Trains models on labeled data to predict outcomes. Commonly used for classification and regression tasks. Examples: Spam detection, image recognition. Unsupervised Learning Discovers patterns in unlabeled data without predefined categories. Examples: Customer segmentation, anomaly detection. Semi-Supervised Learning Combines a small amount of labeled data with a large amount of unlabeled data to improve learning accuracy. Examples: Text classification, image classification. Reinforcement Learning Uses trial and error, allowing agents to learn optimal actions through rewards and penalties. Examples: Game playing, robotics. Deep Learning A subset of machine learning using neural networks with many layers to analyze complex patterns. Examples: Speech recognition, natural language processing.
Top 5 Types of Machine Learning
