Machine Learning Algorithms
Machine learning algorithms are computational methods that enable computer systems to learn from data, identify patterns, and make predictions or decisions without being explicitly programmed for each task. These algorithms are the foundation of artificial intelligence applications, allowing systems to improve their performance as they are exposed to more data.
Machine learning algorithms are broadly categorized into three main types:
Supervised Learning Algorithms:
These algorithms learn from labeled data, where the desired output is known for each input. The goal is to learn a mapping function from input to output so that the algorithm can accurately predict outputs for new, unseen data.Examples: Linear Regression, Logistic Regression, Support Vector Machines (SVMs),
Decision Trees, Random Forests, K-Nearest Neighbors (KNN), Neural Networks.
Common Tasks: Classification (predicting categories) and Regression (predicting continuous values).
Unsupervised Learning Algorithms:
These algorithms work with unlabeled data, aiming to discover hidden patterns, structures, or relationships within the data without prior knowledge of the output.
Examples: K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), Association Rule Mining.
Common Tasks: Clustering (grouping similar data points), Dimensionality Reduction (reducing the number of features), Anomaly Detection (identifying unusual data points).
Reinforcement Learning Algorithms:
These algorithms learn by interacting with an environment, receiving feedback in the form of rewards or penalties for their actions. The goal is to learn a policy that maximizes cumulative reward over time.
Examples: Q-Learning, SARSA, Deep Q-Networks (DQNs).
Common Tasks: Training agents to perform optimal actions in dynamic environments, such as in robotics or game playing.
Beyond these core categories, there are also Semi-supervised Learning algorithms, which utilize a combination of labeled and unlabeled data, and Ensemble Methods, which combine multiple machine learning models to improve overall performance, such as Random Forests and Gradient Boosting.
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