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Python Projects Awesome

Here is the list of all the projects

This repository contains a curated list of all Python projects completed over the course of 100 days as part of our Python projects initiative.

Resources ✔

Books that we recommend

Book Title Author(s) Topics Covered Level
Python Crash Course Eric Matthes Python Basics, Projects Beginner
Automate the Boring Stuff with Python Al Sweigart Practical Python Programming, Automation Beginner
Python Data Science Handbook Jake VanderPlas Data Science, NumPy, Pandas, Matplotlib Intermediate
Fluent Python Luciano Ramalho Advanced Python, Best Practices Advanced
Introduction to Algorithms Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein Data Structures, Algorithms Intermediate to Advanced
Python for Data Analysis Wes McKinney Data Analysis, Pandas, NumPy Intermediate
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Aurélien Géron Machine Learning, Deep Learning Intermediate to Advanced
Grokking Algorithms Aditya Bhargava Algorithms, Data Structures Beginner to Intermediate
Deep Learning with Python François Chollet Deep Learning, Keras Intermediate to Advanced
Python Machine Learning Sebastian Raschka, Vahid Mirjalili Machine Learning, Deep Learning Intermediate to Advanced
Artificial Intelligence: A Modern Approach Stuart Russell, Peter Norvig AI Concepts, Techniques, Applications Advanced
Effective Python Brett Slatkin Best Practices, Pythonic Code Intermediate
Elements of Programming Interviews in Python Adnan Aziz, Tsung-Hsien Lee, Amit Prakash Algorithms, Data Structures, Coding Interviews Intermediate to Advanced
Python Tricks: The Book Dan Bader Tips and Tricks, Best Practices Intermediate
Programming Collective Intelligence Toby Segaran Machine Learning, Data Mining, AI Intermediate

Youtube channels that we recommend

Channel Name URL Description Level
Corey Schafer Link Comprehensive Python tutorials and projects Beginner to Intermediate
freeCodeCamp.org Link Full Python courses and project-based learning Beginner to Intermediate
sentdex Link Python programming, data science, machine learning Intermediate to Advanced
Programming with Mosh Link Python tutorials covering basics and advanced topics Beginner to Intermediate
CS Dojo Link Python programming, algorithms, and data structures Beginner to Intermediate
Tech With Tim Link Python tutorials, game development, and projects Beginner to Intermediate
The Net Ninja Link Python basics, Django, and Flask tutorials Beginner to Intermediate
Traversy Media Link Python tutorials, full-stack development Beginner to Intermediate
Real Python Link Python tips, tutorials, and best practices Intermediate to Advanced
Edureka Link Python for data science, machine learning, AI Intermediate
Clever Programmer Link Python tutorials, web development, and projects Beginner to Intermediate
Data School Link Python for data analysis, pandas, machine learning Intermediate
Telusko Link Python basics, OOP, advanced topics Beginner to Intermediate
Krish Naik Link Python for machine learning, deep learning, AI Intermediate to Advanced
Joma Tech Link Python programming, career advice in tech Beginner to Intermediate

Websites for practice and learn

Website Name URL Description Level
Codecademy Link Interactive Python courses Beginner
Coursera Link Python courses from top universities and institutions Beginner to Advanced
edX Link Python courses from universities Beginner to Advanced
Udemy Link Wide range of Python courses and tutorials Beginner to Advanced
HackerRank Link Python practice problems and coding challenges Intermediate
LeetCode Link Python coding challenges and algorithms practice Intermediate to Advanced
Real Python Link In-depth Python tutorials, articles, and videos Intermediate to Advanced
Python.org Link Official Python documentation and tutorials All Levels
GeeksforGeeks Link Python tutorials, algorithms, and data structures Beginner to Advanced
W3Schools Link Python basics and interactive tutorials Beginner
DataCamp Link Python for data science and analytics Beginner to Intermediate
Kaggle Link Python for data science, machine learning competitions Intermediate
Pluralsight Link Python courses covering various topics Beginner to Advanced
Sololearn Link Interactive Python tutorials and exercises Beginner
Exercism Link Python exercises with mentor feedback Intermediate

Contact

Information on how to get in touch with the repository maintainer(s). Join our online community - matrix.org

Contributing to Python Learn Repository

Thank you for your interest in contributing to the Python Learn repository! We appreciate your efforts to help improve the content and resources for learners. Please follow these guidelines to contribute effectively.

How to Contribute

  1. Fork the Repository:

    • Click the "Fork" button at the top right corner of this repository to create a copy of the repository on your own GitHub account.
  2. Clone the Repository:

    • Clone your forked repository to your local machine using the following command:
      git clone https:/your-username/python-learn.git
  3. Create a New Branch:

    • Create a new branch for your contribution using the following command:
      git checkout -b your-branch-name
  4. Make Your Changes:

    • Add new content, fix bugs, improve documentation, or make any other relevant changes.
    • Ensure your code follows the repository's style guidelines and conventions.
  5. Commit Your Changes:

    • Commit your changes with a clear and descriptive commit message:
      git add .
      git commit -m "Description of your changes"
  6. Push Your Changes:

    • Push your changes to your forked repository:
      git push origin your-branch-name
  7. Create a Pull Request:

    • Go to the original repository on GitHub and click on the "New Pull Request" button.
    • Select your branch from the "compare" dropdown.
    • Provide a clear description of your changes and submit the pull request.

Contribution Guidelines

Code Contributions

  • Style: Follow the PEP 8 style guide for Python code.
  • Testing: Ensure that your code passes all tests. Add new tests if applicable.
  • Documentation: Update documentation and comments where necessary.

Content Contributions

  • Quality: Ensure that the content is clear, concise, and accurate.
  • Relevance: Make sure the content is relevant to the repository’s purpose.
  • Attribution: Give proper credit if you are using external sources or references.

Issue Reporting

  • Search Existing Issues: Before opening a new issue, please check if it already exists.
  • Provide Details: When reporting an issue, include as much detail as possible to help us understand and resolve it.

Pull Request Review

  • Feedback: Be open to feedback and ready to make necessary changes.
  • Respect: Be respectful and considerate in your interactions with other contributors and maintainers.

Community Guidelines

  • Respect: Treat everyone with respect. Be polite and considerate in your communication.
  • Collaboration: Be open to collaboration and willing to help others.
  • Inclusivity: Strive to create an inclusive and welcoming environment for everyone.

Thank you for contributing to the Python Learn repository! Your efforts help make this a valuable resource for learners around the world.

Project List