Machine learning (ML) is a subset of artificial intelligence (AI) that involves the development of algorithms that enable computers to learn from and make predictions or decisions based on data. Instead of being explicitly programmed for every task, ML algorithms build models based on sample data, known as training data, to make data-driven predictions or decisions. Key Concepts in Machine Learning Types of Machine Learning: Supervised Learning: The algorithm is trained on a labeled dataset, meaning that each training example is paired with an output label. Common tasks include classification and regression. Example: Predicting house prices based on features like size, location, and number of bedrooms. Unsupervised Learning: The algorithm works on unlabeled data and tries to find hidden patterns or intrinsic structures in the input data. Common tasks include clustering and association. Example: Grouping customers into different segments based on purchasing behavior. Semi-supervised Learning: Combines a small amount of labeled data with many unlabeled data during training. It falls between supervised and unsupervised learning. Reinforcement Learning: The algorithm learns by interacting with an environment, receiving rewards or penalties for actions, and aims to maximize cumulative rewards. Example: Training a robot to navigate a maze. Common Algorithms: Linear Regression: Used for regression tasks; models the relationship between a dependent variable and one or more independent variables. Logistic Regression: Used for binary classification problems. Decision Trees: Non-linear models that split data into branches to make predictions. Support Vector Machines (SVM): Used for classification and regression tasks by finding the hyperplane that best divides a dataset into classes. K-Nearest Neighbors (KNN): A simple, instance-based learning algorithm for classification and regression.