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Machine learning algorithms are trained according to data to make predictions or decisions. Deep learning is part of ML and uses artificial neural networks (ANNs) to learn from data. The standard approach to expert-in-the-loop machine learning is active learning, where, repeatedly, an expert is asked to annotate one or more records and the machine finds a classifier that respects all annotations made until that point. Azure Machine Learning. Azure Machine Learning provides an environment to create and manage the end-to-end life cycle of Machine Learning models. Azure Machine Learning’s compatibility with open-source frameworks and platforms like PyTorch and TensorFlow makes it an effective all-in-one platform for integrating and handling data and models. Training a machine learning (ML) model is a process in which a machine learning algorithm is fed with training data from which it can learn. 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Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.Training set: The train set is used to train the machine learning model. It contains the data that the model will use to learn the relationships useful for the prediction. Validation set: The validation set is used to evaluate the performance of the machine learning model during training and to test its hyperparametersSpecifically, the learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0. The learning rate controls how quickly the model is adapted to the problem.Police Dog Basic Training - K-9 cops can sniff out drugs, bombs and suspects that would leave human cops ransacking entire cities. 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