Python is a giant in terms of programming languages. It has its roots in every domain, be it web development, computer app development, scripting, or the one thing that make it completely unbeatable is its implementation in machine learning.

Python has very vast online community. By the time this article was written Python (v3.9.0) was the latest release. That means Python v4 is about to come.

Python is widely popular and its easy to use syntax also played a card in its popularity.

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Did you know?

Python is a widely-used, interpreted, object-oriented, and high-level programming language with dynamic semantics, used for general-purpose programming. It was created by Guido van Rossum, and first released on February 20, 1991

Source: Python Institute

GitHub contains awesome reposities that will teach you how to code in Python in a better as all examples are in practical way, and hands-on experience.

Let’s start 👍

1. Awesome-Python

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awesome-python (this link opens in a new window) by vinta (this link opens in a new window)

A curated list of awesome Python frameworks, libraries, software and resources

A curated list of awesome Python frameworks, libraries, software and resources.

You can check this GitHub repo 👉 here

2. The Algorithms

All algorithms implemented in Python (for education)

These implementations are for learning purposes only. Therefore they may be less efficient than the implementations in the Python standard library.

You can check this GitHub repo 👉 here

3. Project-Based Learning

A list of programming tutorials in which learners build an application from scratch. These tutorials are divided into different primary programming languages. Some have intermixed technologies and languages.

You can check this GitHub repo 👉 here

4. Tensorflow

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of toolslibraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google’s Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.

You can check this GitHub repo 👉 here

5. Python-programming-exercises

100+ Python challenging programming exercises.

You can check this GitHub repo 👉 here

6. learn-python3

This repository contains a collection of materials for teaching/learning Python 3 (3.5+).

Requirements

  • Have Python 3.5 or newer installed. You can check the version by typing python3 --version in your command line. You can download the latest Python version from here.
  • Have Jupyter Notebook installed.

If you can not access Python and/or Jupyter Notebook on your machine, you can still follow the web based materials. However, you should be able to use Jupyter Notebook in order to complete the exercises.

Source: GitHub

Usage

  1. Clone or download this repository.
  2. Run jupyter notebook command in your command line in the repository directory.
  3. Jupyter Notebook session will open in the browser and you can start navigating through the materials.

7. learn-python

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learn-python (this link opens in a new window) by trekhleb (this link opens in a new window)

📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations.

📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations.

This is a collection of Python scripts that are split by topics and contain code examples with explanations, different use cases and links to further readings.

It is a playground because you may change or add the code to see how it works and test it out using assertions. It also allows you to lint the code you’ve wrote and check if it fits to Python code style guide. Altogether it might make your learning process to be more interactive and it might help you to keep code quality pretty high from very beginning.

It is a cheatsheet because you may get back to these code examples once you want to recap the syntax of standard Python statements and constructions. Also because the code is full of assertions you’ll be able to see expected functions/statements output right away without launching them.

You might also be interested in 🤖 Interactive Machine Learning Experiments

How to Use This Repository

Each Python script in this repository has the following structure:

"""Lists  <--- Name of the topic here

# @see: https://www.learnpython.org/en/Lists  <-- Link to further readings goes here

Here might go more detailed explanation of the current topic (i.e. general info about Lists).
"""


def test_list_type():
    """Explanation of sub-topic goes here.
    
    Each file contains test functions that illustrate sub-topics (i.e. lists type, lists methods).
    """
    
    # Here is an example of how to build a list.  <-- Comments here explain the action
    squares = [1, 4, 9, 16, 25]
    
    # Lists can be indexed and sliced. 
    # Indexing returns the item.
    assert squares[0] == 1  # <-- Assertions here illustrate the result.
    # Slicing returns a new list.
    assert squares[-3:] == [9, 16, 25]  # <-- Assertions here illustrate the result.

You can check this GitHub repo 👉 here

8. Full-Speed-Python

Full Speed Python: a book for self-learners

Pdf and epub files can be downloaded from: https://github.com/joaoventura/full-speed-python/releases/

This book aims to teach the Python programming language using a practical approach. Its method is quite simple: after a short introduction to each topic, the reader is invited to learn more by solving the proposed exercises.

These exercises have been used extensively in my web development and distributed computing classes at the Superior School of Technology of Setúbal. With these exercises, most students are up to speed with Python in less than a month. In fact, students of the distributed computing course, taught in the second year of the software engineering degree, become familiar with Python’s syntax in two weeks and are able to implement a distributed client-server application with sockets in the third week.

You can check this GitHub repo 👉 here

9. Python_reference

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A collection of useful scripts, tutorials, and other Python-related things

You can check this GitHub repo 👉 here

10. Coding-problems

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coding-problems (this link opens in a new window) by MTrajK (this link opens in a new window)

Solutions for various coding/algorithmic problems and many useful resources for learning algorithms and data structures

Solutions for various coding/algorithmic problems and many useful resources for learning algorithms and data structures

Here you can find solutions for various coding/algorithmic problems and many useful resources for learning algorithms and data structures.
Also, this repo will be updated with new solutions and resources from time to time.

Note that this repo is meant to be used for learning and researching purposes only and it is not meant to be used for production.

Solutions

Algorithms and data structures are not language-specific (it’s true that some languages are faster, and some are easier to use), but if you are good with the logic and pseudocode, any language would be good.

You can check this GitHub repo 👉 here

Bonus Repo 😅👇

“Well who doesn’t like bonuses? Actually there are tremendous amazing repositories available on GitHub, that this list will be never ending.

11. 30-Days-of-Python

A New Version of 30 Days of Python is nearly here. Get started today.

30 Days of Python

For the next 30 days, learn the Python Programming language. Watch the official tutorial series on CFE or YouTube.

 30 Days of Python using Python 3.6 has been moved to here. Happy Coding

You can check this GitHub repo 👉 here

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Conclusion

Open Source can be powerful, name a thing, it will be preferably present on GitHub.

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