Machine learning (ML) tools encompass a broad range of software, libraries, and platforms that enable users to develop, train, and deploy ML models. These tools abstract away much of the underlying complexity of ML algorithms, making it easier for developers and data scientists to apply ML to a variety of tasks. Common uses include predictive modeling, image recognition, natural language processing, and anomaly detection. Some tools provide a graphical user interface (GUI) for model building, while others focus on providing a code-based environment. Popular examples include TensorFlow, PyTorch, scikit-learn, and cloud-based ML platforms like Amazon SageMaker and Google Cloud AI Platform.
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