🚀 Master Data Science & Programming!

Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!


🔰 Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer

🔖 Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM

🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4

🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ

💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1

🧑‍🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC

😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT

💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9

🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab

🖊 Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN

📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV

📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX

🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.me/Python53

⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY

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Admin: @HusseinSheikho

Python library for lightweight robot simulation, designed for navigation, control, and reinforcement learning tasks. 🎧

Most robotics simulators are powerful but quite heavy. Sometimes you just want a minimal environment where you can test navigation, control, or RL without a full-fledged physics engine.

IR-Sim exactly meets this need if you're working with robot navigation or AI.

Why it's worth paying attention to:

• Quick installation and simple configuration
• Scenarios are defined using regular YAML
• Real-time visualization via Matplotlib
• Built-in collision detection
• Suitable for AI and RL pipelines

What you can quickly prototype:

– Multi-robot collision avoidance
– Lidar-based navigation
– Dynamic scenes with moving obstacles
– Testing high-level behavior before moving to a heavy simulator

The project is open-source under MIT, actively developed, and already used in a number of scientific works on navigation and planning.

GitHub: https://github.com/hanruihua/ir-sim
Documentation: https://ir-sim.readthedocs.io/en

If you're a student, researcher, or just studying navigation algorithms, IR-Sim provides a convenient sandbox where you can quickly test ideas without installing bulky dependencies.

👉 @DataScienceN

The tutorial on Basketball AI for YouTube is finally out. How to analyze one play frame by frame using a CV pipeline for sports analytics. 
🤟 

More than 1000 hours of work condensed into 37 minutes. 

- player detection via RF-DETR 
- player tracking via SAM2 
- team clustering via SigLIP and K-means 
- number recognition via SmolVLM2 

The system can be reused as a base for your own projects (analytics, highlights, broadcasts, etc.). 

Watch here 

👉  @DataScienceN ❤️