Packages.md

Commonly Used AI/ML Python Libraries

This README provides an overview of the most common libraries used in data science, machine learning, computer vision, and deep learning, along with instructions on how to install them using pip.

Table of Contents

1. Data Science Libraries
2. Machine Learning Libraries
3. Computer Vision Libraries
4. Deep Learning Libraries
5. Installation Instructions

💡Incase you have installed pip and want to download all packages in one go without reading much about it, run this command directly onto your terminal:

pip install numpy pandas matplotlib seaborn scipy scikit-learn xgboost lightgbm catboost opencv-python scikit-image pillow tensorflow keras torch torchvision mxnet

Data Science Libraries

1. NumPy

   • Description: Fundamental package for scientific computing with Python.
   • Installation: pip install numpy

2. Pandas

   • Description: Data manipulation and analysis library.
   • Installation: pip install pandas

3. Matplotlib

   • Description: Comprehensive library for creating static, animated, and interactive visualizations.
   • Installation: pip install matplotlib

4. Seaborn

   • Description: Statistical data visualization based on Matplotlib.
   • Installation: pip install seaborn

5. SciPy

   • Description: Library for mathematics, science, and engineering.
   • Installation: pip install scipy

Machine Learning Libraries

1. Scikit-learn

   • Description: Simple and efficient tools for predictive data analysis.
   • Installation: pip install scikit-learn

2. XGBoost

   • Description: Scalable and flexible gradient boosting library.
   • Installation: pip install xgboost

3. LightGBM

   • Description: High-performance gradient boosting framework.
   • Installation: pip install lightgbm

4. CatBoost

   • Description: Gradient boosting on decision trees library.
   • Installation: pip install catboost

Computer Vision Libraries

1. OpenCV

   • Description: Library of programming functions mainly aimed at real-time computer vision.
   • Installation: pip install opencv-python

2. scikit-image

   • Description: Collection of algorithms for image processing.
   • Installation: pip install scikit-image

3. Pillow

   • Description: Python Imaging Library (PIL) adds image processing capabilities.
   • Installation: pip install pillow

Deep Learning Libraries

1. TensorFlow

   • Description: End-to-end open-source platform for machine learning.
   • Installation: pip install tensorflow

2. Keras

   • Description: Deep learning API written in Python, running on top of TensorFlow.
   • Installation: pip install keras

3. PyTorch

   • Description: Deep learning platform that provides maximum flexibility and speed.
   • Installation: pip install torch torchvision

4. MXNet

   • Description: Deep learning framework designed for both efficiency and flexibility.
   • Installation: pip install mxnet

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Installation Instructions

To install any of the above libraries, you can use the pip command followed by the library name. Here are general steps to install these libraries:

1. Open your command line or terminal.

2. Type the following command to install the desired library:

   pip install <library_name>

   Replace <library_name> with the name of the library you wish to install. For example, to install NumPy, you would use:

   pip install numpy

3. Wait for the installation to complete.

Example Installation

To install multiple libraries at once, you can list them all in a single pip install command:

pip install numpy pandas matplotlib seaborn scipy scikit-learn xgboost lightgbm catboost opencv-python scikit-image pillow tensorflow keras torch torchvision mxnet

This command will install all the listed libraries in one go.

Additional Resources

For more detailed documentation and tutorials on each library, refer to their official websites:

• NumPy Documentation
• Pandas Documentation
• Matplotlib Documentation
• Seaborn Documentation
• SciPy Documentation
• Scikit-learn Documentation
• XGBoost Documentation
• LightGBM Documentation
• CatBoost Documentation
• OpenCV Documentation
• scikit-image Documentation
• Pillow Documentation
• TensorFlow Documentation
• Keras Documentation
• PyTorch Documentation
• MXNet Documentation

This README should help you get started with installing and using some of the most popular libraries in data science, machine learning, computer vision, and deep learning. Happy coding!