Machine Learning Techniques
Machine Learning Techniques refer to a set of algorithms and statistical models that enable computer systems to automatically improve their performance on a specific task by learning from data, without being explicitly programmed. These techniques are used to analyze and extract insights from large datasets, and to build predictive models that can be used to make accurate predictions or decisions. Machine Learning Techniques can be broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model on labeled data, where the correct output is already known. Unsupervised learning involves training a model on unlabeled data, where the model must identify patterns and relationships on its own. Reinforcement learning involves training a model to make decisions based on feedback from its environment. Machine Learning Techniques are widely used in various fields, including computer vision, natural language processing, speech recognition, and robotics.
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