Python Libraries

  • Bokeh: A library for creating interactive web-based visualizations, allowing users to explore data dynamically in a web browser.
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  • Keras: A user-friendly neural network library that acts as a high-level API for TensorFlow, making it easier to define and train deep learning models.
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  • Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations in various formats.
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  • NumPy: A fundamental library for numerical computing, providing support for large, multi-dimensional arrays and matrices, along with high-level mathematical functions.
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  • Pandas: A powerful library for data manipulation and analysis, providing data structures like DataFrames for handling and processing structured data efficiently.
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  • Plotly: Another interactive visualization library that supports a wide range of chart types and can be used for creating dashboards and web applications.
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  • PyTorch: Another popular open-source machine learning framework, known for its flexibility, dynamic computation graphs, and strong support for GPU acceleration.
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  • Scikit-learn: A comprehensive machine learning library offering tools for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
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  • SciPy: Built on top of NumPy, SciPy offers a collection of algorithms and mathematical tools for scientific and technical computing, including optimization, integration, interpolation, and signal processing.
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  • Seaborn: A statistical data visualization library built on top of Matplotlib, providing a high-level interface for creating informative and attractive statistical graphics.
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  • TensorFlow: An open-source machine learning framework developed by Google, widely used for building and training deep learning models.
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