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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  • Input/output Devices: Input/output (I/O) devices are hardware components that allow users to interact with a computer system. Input devices are used to provide data and control signals to a computer, while output devices display or transmit the results of proces ... Read More >>
  • Event Processing: Event processing is a method of tracking and analyzing streams of data in real-time to identify meaningful patterns, trends, and insights. It involves capturing, filtering, aggregating, and analyzing data from various sources such as sensor ... Read More >>
  • Stochastic Gradient Descent: Stochastic Gradient Descent (SGD) is an iterative optimization algorithm used to minimize the cost function in machine learning and deep learning models. It is a variant of gradient descent algorithm that updates the model parameters using ... Read More >>
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