Machine learning trains algorithms to find patterns in data and make predictions. Instead of programming explicit rules, you feed the algorithm examples. It learns the relationship between inputs and outputs. Then it applies that learning to new data. A spam filter learns from millions of labeled emails. A recommendation engine learns from user behavior. A fraud model learns from historical transactions. The algorithm improves as it sees more data, up to a point.

The main types are supervised, unsupervised, and reinforcement learning. Supervised learning uses labeled data. The algorithm learns to map inputs to known outputs. Classification predicts categories. Regression predicts numbers. Unsupervised learning finds structure in unlabeled data. Clustering groups similar records. Dimensionality reduction simplifies complex data. Reinforcement learning trains an agent through trial and error, rewarding good actions and penalizing bad ones. It powers game-playing systems and robotics. Each type suits different problems. The quality of the training data determines the quality of the model. Biased data produces biased predictions. Incomplete data produces unreliable ones. Overfitting is the constant risk. A model that memorizes the training data performs poorly on new data. Regularization, cross-validation, and more data help. Machine learning is powerful and fragile. It finds patterns, including ones that should not exist.

Machine learning types

• Supervised — labeled data, classification and regression
• Unsupervised — unlabeled data, clustering and dimensionality reduction
• Reinforcement — trial and error with rewards
• Deep learning — neural networks with many layers

Machine learning is pattern recognition at scale. The pattern is only as good as the data that produced it.