Sign in Subscribe

python for ml

Last updated on 


Python is a popular programming language for machine learning (ML) due to its simplicity, versatility, and the availability of numerous libraries and frameworks specifically designed for ML tasks. Here are some key libraries and frameworks in Python that are commonly used in machine learning:

  1. NumPy: A fundamental package for scientific computing with Python. It provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays.
  2. Pandas: A data manipulation and analysis library. It provides data structures for efficiently storing and manipulating large datasets, making it an essential tool for data preprocessing and cleaning.
  3. Matplotlib and Seaborn: These libraries are used for data visualization. Matplotlib is a comprehensive 2D plotting library, while Seaborn is built on top of Matplotlib and provides a high-level interface for drawing attractive and informative statistical graphics.
  4. Scikit-learn: A simple and efficient machine learning library that includes various tools for classification, regression, clustering, dimensionality reduction, and more. It's built on NumPy, SciPy, and Matplotlib.
  5. TensorFlow and PyTorch: These are powerful open-source deep learning frameworks. They provide tools for building and training neural networks, making them essential for tasks like image recognition, natural language processing, and other deep learning applications.
  6. Keras: Originally developed as a high-level interface for building neural networks on top of other deep learning frameworks, Keras is now integrated with TensorFlow and provides a user-friendly API for building and training deep learning models.
  7. Scipy: A library used for scientific and technical computing. It builds on NumPy and provides additional modules for optimization, signal and image processing, statistical functions, and more.
  8. Statsmodels: A library for estimating and testing statistical models. It includes various statistical models, tests, and data exploration tools.
  9. Jupyter Notebooks: While not a library, Jupyter Notebooks are widely used for interactive data analysis and development in Python. They allow you to create and share documents that contain live code, equations, visualizations, and narrative text.