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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- Missing At Random: Missing at random (MAR) is a mechanism that describes the missingness of data in a dataset. In MAR, the probability of a data point being missing depends on the observed data in the dataset. This means that the missingness is not completely ... Read More >>
- Unlabeled Data: In the context of data science, unlabeled data refers to a dataset that does not have any predefined categories or labels. This means that the data has not been classified or organized in any way that would allow a machine learning algorith ... Read More >>
- Client-side: Client-side refers to the part of a software application that runs on the user's device, such as a web browser or mobile app. In the context of data science and artificial intelligence, client-side processing can refer to the use of client- ... Read More >>
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