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ML libraries useful for data engineering tasks.
Machine learning libraries in Python are collections of modules and functions that simplify the process of implementing machine learning algorithms. These libraries provide tools for data preprocessing, algorithm implementation, model evaluation, and prediction, streamlining the development of machine learning models. They are used in various domains, including image and speech recognition, natural language processing, and predictive analytics, enabling data scientists and developers to build, train, and deploy machine learning models efficiently. Popular libraries like Scikit-learn, TensorFlow, and PyTorch offer a range of functionalities from basic to advanced, catering to diverse machine learning tasks and requirements.
Machine Learning in Python
Versatile library providing a range of supervised and unsupervised learning algorithms. Known for its ease of use and efficiency for data mining and data analysis with classical ML algorithms.
End-to-End ML Platform
End-to-end open-source platform for machine learning enabling complex computations with data flow graphs. Widely used for deep learning applications with robust production support.