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Machine learning (ML) is a subset of Artificial Intelligence (AI) that enables systems to learn and make predictions or decisions based on data without explicit programming. By analyzing data patterns, ML systems can adapt and improve over time, becoming more accurate in their responses.
Types of machine learning
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
1.Supervised learning
In supervised learning, computers are trained using labeled data where both input and output are known. The system learns to map inputs to their correct outputs by identifying patterns in the data. During training, the model adjusts its parameters to minimize errors in predictions. This process continues until the model achieves a desired level of accuracy. Once trained, the model can make accurate predictions or decisions when presented with new, unseen data.
Example
2.Unsupervised learning
In unsupervised learning, the system learns from data without labeled outputs. Instead of being told what to predict, the model identifies hidden patterns and relationships within the dataset. For instance, when working with a large dataset, unsupervised learning can help group similar data points by recognizing underlying structures or similarities. This method is commonly used in clustering, anomaly detection, and association problems, where the goal is to explore the data and find patterns without predefined categories or outcomes.
Example
3.Semi-supervised learning
In semi-supervised learning, a model is trained on a dataset where only a small portion of the data is labeled, and the rest is unlabeled. This method leverages the labeled data to guide the learning process while also exploring patterns in the unlabeled data. It's particularly useful when labeling data is expensive or time-consuming, as it combines the strengths of supervised and unsupervised learning to improve accuracy while reducing the need for extensive labeled datasets.
Example
4.Reinforcement learning
Reinforcement learning is a type of machine learning where algorithms learn by interacting with their environment through trial and error. It uses a reward and punishment system to guide decision-making, allowing the model to learn optimal behavior. In simpler terms, the model's output depends on its current state, and future actions are influenced by the results of previous actions. The goal is to maximize cumulative rewards over time by taking the best possible actions based on the feedback received.
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