Table of Contents
Types of Machine-Leaning
In supervised learning, the algorithm learns from labeled data, meaning each input is associated with a corresponding output. The goal is to learn a mapping function from input to output, so that when given new inputs, the algorithm can predict the correct output. Examples of supervised learning tasks include classification (assigning labels to input data) and regression (predicting continuous values).
Examples: Email spam detection, image classification (e.g., cat vs. dog), predicting house prices based on features.
In unsupervised learning, the algorithm learns from unlabeled data, meaning there are no predefined output labels. Instead, the algorithm looks for patterns, structures, or relationships in the data. Unsupervised learning can be used for tasks such as clustering (grouping similar data points together) and dimensionality reduction (reducing the number of features while preserving important information).
Examples: Customer segmentation, anomaly detection, topic modeling.
In unsupervised learning, the algorithm learns from unlabeled data, meaning there are no predefined output labels. Instead, the algorithm looks for patterns, structures, or relationships in the data. Unsupervised learning can be used for tasks such as clustering (grouping similar data points together) and dimensionality reduction (reducing the number of features while preserving important information).
Examples: Customer segmentation, anomaly detection, topic modeling.
The algorithm learns by interacting with an environment to achieve a goal. The algorithm receives feedback in the form of rewards or penalties based on its actions, and its objective is to learn the optimal policy (sequence of actions) to maximize cumulative reward over time. Reinforcement learning is often used in scenarios where there is no labeled data available, and the agent learns through trial and error.
Examples: Training autonomous agents (e.g., robots or game-playing algorithms), optimizing resource allocation in dynamic environments, and controlling processes such as industrial automation.
