10 great Python packages for Data Science not known to many:

1️⃣ CleanLab
You're missing out on a lot if you haven't started using Cleanlab yet!
Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.
It's like a magic wand! 🪄✨

Check this out👇
https://lnkd.in/dY2fp5YW

2️⃣ LazyPredict
A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.
Supports both regression & classification! ✨

Check this out👇
https://lnkd.in/ggZ-HByv

3️⃣ Lux
A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.

Check this out👇
https://lnkd.in/genaV395

4️⃣ PyForest
A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.

Check this out👇
https://lnkd.in/gv2tsjMe

5️⃣ PivotTableJS
PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥

Check this out👇
https://lnkd.in/dapGg-AS

6️⃣ Drawdata
Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.
Very handy for learning & understanding the behaviour of ML algorithms!

Check this out👇
https://lnkd.in/gBSrQ-e4

7️⃣ black
The Uncompromising Code Formatter
Arguably the best, I use it everyday!!

Check this out 👇
https://lnkd.in/gsz2Mxqn

8️⃣ PyCaret
An open-source, low-code machine learning library in Python that automates the machine learning workflow.

Check this out👇
https://lnkd.in/gUzh-YZM

9️⃣ PyTorch-Lightning by @LightningAI⚡️
If you like PyTorch, you'll love PyTorch Lightning!
Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.

Check this out👇
https://lnkd.in/dir2Xej7

🔟 Reflex
Build Performant, customizable web apps in pure Python that you can deploy in seconds
Reflex is a library to build full-stack web apps in pure Python.

Check this out👇
https://lnkd.in/gz_vsNba

https://t.me/CodeProgrammer 🆘

🏳️‍🌈 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.

➖ ➖ ➖

🖥 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