Ml

Machine Learning (often abbreviated as ML) is a subfield of Artificial Intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming.

Overview

Machine Learning is based on the concept that systems can learn from data, identify patterns, and make decisions with minimal human intervention. It is closely related to computational statistics, which focuses on making predictions using computers, and data mining, which involves finding patterns in large datasets.

Types of Machine Learning

Machine Learning can be broadly classified into three categories: Supervised learning, Unsupervised learning, and Reinforcement learning.

Supervised Learning

In Supervised Learning, the model is trained on a labelled dataset. That is, the algorithm learns from the data which is already known, to predict future events. It can be further divided into classification and regression problems.

Unsupervised Learning

Unsupervised Learning, on the other hand, deals with unlabelled data. The system tries to learn without a teacher. The most common unsupervised learning method is cluster analysis.

Reinforcement Learning

Reinforcement Learning is a type of Machine Learning where an agent learns to behave in an environment, by performing certain actions and observing the results.

Applications of Machine Learning

Machine Learning has a wide range of applications including speech recognition, medical diagnosis, statistical arbitrage, learning association rules, genetic algorithms, and search engines.

See Also

• Deep Learning
• Neural Networks
• Artificial Intelligence
• Data Science

References

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