Machine learning (ML)

Machine learning (ML)

Machine learning has changed the way we solve complex problems and make decisions backed by data. It allows systems to learn from patterns and make accurate predictions, driving real impact across industries.

What is machine learning?

Machine learning is a part of artificial intelligence that lets computers learn from data and make decisions without being explicitly programmed. It uses large datasets to train algorithms to spot patterns, draw insights, and improve how decisions are made.

What are the different types of machine learning?

Machine learning is built on two main approaches: supervised and unsupervised learning. These form the foundation of how machines learn from data.

• Supervised Learning

  Supervised learning uses labeled data to train models. Each input is paired with the correct output, helping the algorithm learn the relationship between them. Over time, it learns to make accurate predictions on new, unseen data based on what it has learned from the examples.

• Unsupervised Learning

  Unsupervised learning works with data that has no labels. Instead of learning from known outcomes, these algorithms find patterns, groupings, or structures hidden in the data. Techniques like clustering and dimensionality reduction help make sense of complex datasets by revealing insights that aren’t immediately obvious.

• Reinforcement Learning

  Reinforcement learning is about learning by doing. An agent interacts with its environment, makes decisions, and learns from the results. It gets rewards for good actions and penalties for bad ones, helping it improve over time and reach a specific goal through trial and error.

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