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+Machine learning (ML) is a branch of artificial intelligence that teaches computers to recognize patterns and make decisions from data. Instead of following fixed instructions, an ML model learns through examples. Algorithms such as linear regression, decision trees, and neural networks analyze data to find hidden relationships. The model then predicts outcomes—like whether an email is spam, or how much a house might sell for—based on what it has learned.
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+A typical ML workflow includes gathering data, cleaning it, splitting it into training and testing sets, and evaluating performance using metrics such as accuracy or F1 score. The power of ML lies in iteration: the more relevant data and feedback it receives, the smarter it becomes. Today, ML drives everything from voice assistants and recommendation systems to medical diagnostics and self-driving cars.
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+At its heart, machine learning mirrors human experience: learning from mistakes, adapting to new information, and improving with practice. The field continues to evolve rapidly, blending statistics, computing, and creativity to build systems that not only compute—but learn.