Machine learning algorithms are basically the brains behind computers that learn from data, spot patterns, and make predictions without being directly programmed for each task. They’re grouped into three main types:

⦁ Supervised learning: Learns from labeled data to predict outcomes (e.g., Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, Neural Networks).
⦁ Unsupervised learning: Finds patterns in unlabeled data (e.g., K-means Clustering, Hierarchical Clustering, Association Rules, Principal Component Analysis, Autoencoders).
⦁ Reinforcement learning: Learns by trial and error, getting feedback from actions (great for games and robotics).

Each type has its own popular algorithms and use cases, from predicting house prices to grouping customers by behavior.