Python Development

Django is a high-level Python web framework known for its simplicity, flexibility, and scalability. It follows the Model-View-Controller (MVC) pattern and includes features such as an ORM (Object-Relational Mapper), built-in admin panel, and robust authentication system, making it ideal for building complex web applications.

Flask is a lightweight and versatile Python web framework that is easy to learn and use. It provides a simple and extensible architecture for building web applications and APIs, with features such as URL routing, template rendering, and support for extensions.

NumPy is a fundamental library for scientific computing in Python, providing support for multidimensional arrays, mathematical functions, linear algebra, and random number generation. It is widely used for numerical computations and data analysis tasks.

Pandas is a powerful data manipulation and analysis library for Python, built on top of NumPy. It provides data structures like DataFrames and Series for handling structured data, as well as functions for cleaning, transforming, and analyzing data efficiently.

Matplotlib is a plotting library for Python that enables users to create a wide variety of static, animated, and interactive visualizations. It provides support for creating plots, histograms, scatter plots, and more, making it a valuable tool for data visualization and exploration.

TensorFlow is an open-source machine learning framework developed by Google for building and training deep learning models. It provides a flexible and scalable platform for developing neural networks and other machine learning algorithms, with support for both CPU and GPU acceleration.

PyTorch is another popular open-source machine learning framework that is widely used for research and production-level projects. It provides a dynamic computational graph and supports automatic differentiation, making it easy to build and train deep learning models.

Scikit-learn is a machine learning library for Python that provides simple and efficient tools for data mining and analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more, along with utilities for model evaluation and validation.