Machine learning paradigms encompass a variety of techniques and algorithms used to train models on data. These paradigms include supervised learning, where models are trained on labeled data for tasks like classification and regression, as well as unsupervised learning, which involves clustering and dimensionality reduction. Other paradigms such as reinforcement learning focus on training agents to make sequential decisions through interactions with an environment. Additionally, ensemble learning techniques like boosting and bagging combine multiple models to improve predictive performance.
