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Machine Learning

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.

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  • What is Machine Learning (ML)?

    Machine Learning is a branch of artificial intelligence that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. ML allows systems to improve their performance over time based on experience.

  • How does Machine Learning work?

    Machine Learning works by training models on labeled data, allowing the algorithm to learn patterns and relationships. These trained models can then make predictions or decisions when presented with new, unseen data. The process involves training, testing, and iterating to improve the model's accuracy.

  • What are the types of Machine Learning?

    Types of ML include:
    Supervised Learning: Models learn from labeled data with input-output pairs.
    Unsupervised Learning: Models discover patterns and relationships in unlabeled data.
    Reinforcement Learning: Agents learn through interaction with an environment, receiving rewards or penalties based on actions.
    Semi-Supervised Learning and Self-Supervised Learning: Hybrid approaches using both labeled and unlabeled data.

  • What are common applications of Machine Learning?

    ML is used in various applications, including:
    Image and speech recognition
    Natural language processing
    Recommendation systems
    Fraud detection
    Predictive analytics
    Autonomous vehicles
    Healthcare diagnostics

  • How does Machine Learning use algorithms and models?

    Machine Learning algorithms process data and learn patterns to create models. These models, based on learned parameters, can then make predictions or decisions when presented with new data. Common algorithms include decision trees, neural networks, support vector machines, and clustering algorithms.

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