Machine Learning Algorithm

AutoML (Automated Machine Learning) refers to the use of automated methods to automate the end-to-end process of applying machine learning to real-world problems. It involves automating tasks such as data pre-processing, feature engineering, model selection, hyperparameter tuning, and deployment. AutoML algorithms are designed to help data scientists and machine learning practitioners save time and effort …

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Transfer learning is a machine learning technique that involves leveraging the knowledge learned from one task to improve the performance of another related task. In other words, a model that has been trained on one task can be used as a starting point for training a new model on a related task. Transfer learning can …

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Optimization algorithms used to tune the parameters of a model so that it can make accurate predictions or classifications on new, unseen data. In other words, they are used in machine learning to find the optimal values of the parameters of a model that minimize a cost function or maximize a performance metric. There are …

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Regularization algorithms are techniques used in machine learning to prevent overfitting of a model to the training data. Overfitting occurs when a model is too complex and captures noise and randomness in the training data, resulting in poor generalization to new, unseen data. Regularization algorithms work by adding a penalty term to the cost function …

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Instance-based learning, also known as lazy learning, is a type of supervised learning algorithm where the model learns by comparing new data instances with training examples that are stored in memory. The algorithm does not attempt to learn a general model of the data, but rather memorizes the specific examples seen during training. When a …

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Association rule learning is a type of unsupervised learning algorithm used for discovering interesting relationships or patterns in large datasets. The algorithm works by analyzing the co-occurrence of different items or features in a dataset, and identifying sets of items that tend to appear together. The most common application of association rule learning is in …

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Gradient boosting is a type of ensemble learning algorithm used for classification and regression tasks. The algorithm works by building an ensemble of weak prediction models, typically decision trees, that are trained sequentially to minimize a loss function. In gradient boosting, each new model in the ensemble is trained to predict the residual error of …

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Random forest is a type of ensemble learning algorithm used for classification, regression, and other tasks that involve predicting a target variable. The algorithm works by constructing multiple decision trees, each trained on a random subset of the data and using a random subset of the features. Each decision tree in the random forest algorithm …

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Support Vector Machines (SVMs) are a class of supervised learning algorithms used for classification and regression analysis. SVMs are based on the idea of finding a hyperplane that best separates data into different classes. In binary classification, the hyperplane is a line that separates the two classes in the feature space. SVMs find the hyperplane …

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Neural network algorithms, also known as artificial neural networks or simply “neural networks”, are a class of machine learning algorithms that are modeled after the structure and function of the human brain. They are used for a variety of tasks, including classification, regression, and pattern recognition. Neural networks are composed of layers of interconnected nodes, …

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