متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Data science/ML/AI
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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Data science/ML/AI
تُعد قناة Data science/ML/AI (@datascience_bds) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 13 667 مشتركاً، محتلاً المرتبة 9 391 في فئة التكنولوجيات والتطبيقات والمرتبة 31 743 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 13 667 مشتركاً.
بحسب آخر البيانات بتاريخ 08 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 150، وفي آخر 24 ساعة بمقدار 4، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 7.97%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 2.27% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 1 089 مشاهدة. وخلال اليوم الأول يجمع عادةً 310 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 5.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل panda, learning, row, api, ethic.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Data science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
👉 https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 09 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
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المشتركون
+424 ساعات
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+15030 أيام
أرشيف المشاركات
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Repost from Cool GitHub repositories
lerobot
This is an end-to-end library for robot learning. It handles the entire pipeline from loading and processing robotics datasets to training policies and deploying them in simulation or on real hardware.
Creator: huggingface
Stars ⭐️: 19,000
Forked by: 3,000
Github Repo:
https://github.com/huggingface/lerobot
#robotics #AI
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Join @github_repositories_bds for more cool repositories. This channel belongs to @bigdataspecialist group
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📚 Data Science Riddle
A business team wants interpretable insights, not just predictions. What's the best model to start with?
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Notes on SQL for data management and analysis, including queries and integration with R, from University of South Carolina.
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📚 Data Science Riddle
Why might your SQL join explode the number of rows unexpectedly?
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This is our latest post from Instagram page, saved as PDF.
If you want a very comprehensive breakdown on what's LLMs are and how they actually work, you might want to check it out.
Here's our Instagram post: Explaining LLMs
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Regularization: The Art of Keeping Models Humble
Overfitting is the “ego problem” of models. They memorize training data and forget how to generalize.
Regularization is how we humble them.
➡️ L1 (Lasso): Shrinks some weights to zero → performs feature selection.
➡️ L2 (Ridge): Reduces all weights slightly → smooths learning.
➡️ Dropout: Randomly removes neurons during training → prevents co-dependence.
It’s not about punishment but it’s about discipline.
Regularization teaches models to focus on patterns, not exceptions.
💭 Remember: The best models don’t just fit data. They respect uncertainty.
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📚 Data Science Riddle
You discover your regression model performs poorly on recent data. The relationships between variables have shifted. What's this called?
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List of AI Project Ideas 👨🏻💻
Beginner Projects
🔹 Sentiment Analyzer
🔹 Image Classifier
🔹 Spam Detection System
🔹 Face Detection
🔹 Chatbot (Rule-based)
🔹 Movie Recommendation System
🔹 Handwritten Digit Recognition
🔹 Speech-to-Text Converter
🔹 AI-Powered Calculator
🔹 AI Hangman Game
Intermediate Projects
🔸 AI Virtual Assistant
🔸 Fake News Detector
🔸 Music Genre Classification
🔸 AI Resume Screener
🔸 Style Transfer App
🔸 Real-Time Object Detection
🔸 Chatbot with Memory
🔸 Autocorrect Tool
🔸 Face Recognition Attendance System
🔸 AI Sudoku Solver
Advanced Projects
🔺 AI Stock Predictor
🔺 AI Writer (GPT-based)
🔺 AI-powered Resume Builder
🔺 Deepfake Generator
🔺 AI Lawyer Assistant
🔺 AI-Powered Medical Diagnosis
🔺 AI-based Game Bot
🔺 Custom Voice Cloning
🔺 Multi-modal AI App
🔺 AI Research Paper Summarizer
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🚨 When & How Jupyter Notebooks Fail (And What To Use Instead)
Hey Data Folks! 👩💻👨💻
Let’s talk about Jupyter Notebooks — powerful for exploration, but risky in production. Here’s why:
❌ Problems with Notebooks:
1. Out-of-order execution → hidden bugs.
2. Code changes after execution → inconsistent results.
3. Data leakage → sensitive info in outputs.
4. Security risks → tokens/keys exposed.
5. Hard to apply engineering practices → no modular code, testing, CI/CD.
6. Collaboration pain → merge conflicts, JSON issues.
7. Reproducibility issues → missing dependencies, versions.
✅ When They’re Useful:
- Quick data exploration & prototyping.
- Knowledge sharing (clean, runnable from top to bottom).
- Teaching / hands-on tutorials (with solution notebooks).
🔧 What to Use Instead:
- For production code → .py files + IDEs.
- For workflows → template repos & reproducible setups.
- For deployment → MLOps tools, pipelines, automation.
💡 Key Takeaways:
- Use notebooks for exploration & teaching.
- Use structured code + pipelines for production & deployment.
- Always document dependencies, keep notebooks clean, never commit secrets!
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📚 Data Science Riddle
Your batch ETL job runs slower each week despite no code change. What's your first suspect?
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Hey everyone 👋
Some time ago, I asked if I should start a Data Science educational series and since 96% of you said yes, I began creating it.
But many of you also asked for real, hands-on experience with projects, not just lessons. So I decided to shift gears. It’s now becoming a full practical coding course! 💻
My goal is to help you build skills that get you job-ready, not just teach theory. It’s taking a bit longer, but I promise it’ll be worth it.
Thank you all for your support and patience ❤️
I’ll let you know as soon as we’re ready to start!
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📚 Data Science Riddle
During EDA(Explanatory Data Analysis), what's the main reason we use box plots?
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Discusses Modeling ETL workflows for data warehousing, including data sources and transformations, from Drexel University.
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📚 Data Science Riddle
Why is data validation before model training critical in production ML systems?
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Latest post from our Instagram page, saved as PDF ☝️
You can also find it here: https://www.instagram.com/p/DQJrbCaDBpy/
