dhrumilrana25/python-libraries-tutorial

Comprehensive tutorials on popular Python libraries: NumPy, Pandas, Matplotlib, Seaborn, Keras, TensorFlow, and PyTorch. Easy-to-follow guides with code examples for data manipulation, visualization, and machine learning. Perfect for beginners and experienced developers. Contribute and learn with us!

Python-Libraries-Tutorial

Welcome to my GitHub repository! Here, you will find detailed tutorials on various Python libraries that are commonly used for data manipulation, visualization, and machine learning. Each library tutorial is classified individually for easy navigation and learning. Whether you're a beginner or an experienced Python developer, these tutorials will help you harness the power of popular Python libraries effectively.

Libraries Covered

1. NumPy NumPy is the fundamental package for scientific computing with Python. It provides support for arrays, matrices, and mathematical functions, making it an essential library for data analysis and numerical computations.

2. Pandas Pandas is a powerful and flexible library for data manipulation and analysis. With its DataFrame and Series structures, Pandas simplifies data handling, cleaning, and preparation tasks.

3. Matplotlib Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. It offers a wide range of plotting options to help you present your data effectively.

4. Seaborn Seaborn is a data visualization library built on top of Matplotlib. It provides a higher-level interface for creating attractive statistical graphics, making it easy to generate informative visualizations.

5. Keras Keras is a high-level neural networks API, written in Python, that runs on top of deep learning frameworks like TensorFlow and Theano. It simplifies the process of building and training deep learning models.

7. TensorFlow TensorFlow is an open-source machine learning library developed by Google. It is widely used for building and training deep learning models, including neural networks.

How to Use Each library tutorial is organized into separate folders. Inside each folder, you will find detailed explanations, code examples to help you understand the library's functionalities. Feel free to explore, learn, and experiment with the code provided.

Contribution I encourage you to contribute to this repository by submitting pull requests. If you spot any errors, have suggestions for improvement, or want to add new tutorials for other Python libraries, your contributions are most welcome.

Stay Connected To stay updated with the latest tutorials and repository changes, make sure to Watch this repository. You can also follow me on Twitter for announcements and updates.

Happy learning and happy coding!

Dhrumil Rana [email protected]