🏳️‍🌈 Learning Python for science is
✅ with these 8 awesome GitHub repos!


🖥 Repo: Project Based Learning

💬 One of the most famous educational repos with 230K+ stars that implements various algorithms and projects using Python.

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🖥 Repo: Real Python Materials

💬 Supplementary resources and exercises including project-based tutorials, guides, and practical exercises.

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🖥 Repo: Learn By Doing

💬 Project-based tutorials in AI and machine learning for all levels.

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🖥 Repo: Awesome Jupyter

💬 A curated collection of notebooks, tools, and powerful libraries for working with Jupyter.

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🖥 Repo: Python Mini Projects

💬 A collection of mini-projects like games and small apps that you can quickly run and practice.

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🖥 Repo: 100Projects of Code

💬 An educational challenge including 100 real projects; you practice and see your progress day by day.

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🖥 Repo: Data Science Projects

💬 Practical ideas and examples to start data science with Python.

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🖥 Repo: Python Project Scripts

💬 Small and large scripting projects, from beginner to advanced levels.

By: https://t.me/CodeProgrammer ✈️

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer

This GitHub repository is a real treasure trove of free programming books.

Here you'll find hundreds of books on topics like #AI, #blockchain, app development, #game development, #Python #webdevelopment, #promptengineering, and many more ✋

GitHub: https://github.com/EbookFoundation/free-programming-books

https://t.me/CodeProgrammer ⭐

Python library RetinaFace for face detection and working with key points (eyes, nose, mouth)

Supports face alignment, easily installed via pip install retina-face, and works based on deep models from the insightface project.

An excellent tool for tasks in computer vision and face recognition.

Usage examples:

from retinaface import RetinaFace

resp = RetinaFace.detect_faces("img1.jpg")
print(resp)

{
    "face_1": {
        "score": 0.9993440508842468,
        "facial_area": [155, 81, 434, 443],
        "landmarks": {
          "right_eye": [257.82974, 209.64787],
          "left_eye": [374.93427, 251.78687],
          "nose": [303.4773, 299.91144],
          "mouth_right": [228.37329, 338.73193],
          "mouth_left": [320.21982, 374.58798]
        }
  }
}

👉 @DataScienceN