What are two techniques of Machine Learning ?

There are many techniques in machine learning, but two fundamental ones are:

1. Supervised Learning: This technique involves learning a mapping from input data to output labels based on example input-output pairs. In supervised learning, the algorithm is trained on a labeled dataset, meaning it learns from data that already has correct answers. The goal is to generalize this learning to make predictions or classifications on new, unseen data.
2. Unsupervised Learning: Unlike supervised learning, unsupervised learning involves learning patterns and structures from unlabeled data. The algorithm attempts to find hidden structure in the input data without explicit feedback. Common tasks in unsupervised learning include clustering, where the algorithm groups similar data points together, and dimensionality reduction, which involves reducing the number of variables in a dataset while preserving its important features.

These two techniques form the foundation of many machine learning approaches, and many other methods, such as semi-supervised learning, reinforcement learning, and deep learning, can be seen as extensions or combinations of these basic principles.