اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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
نمایش بیشتر📈 تحلیل کانال تلگرام Data science/ML/AI
کانال Data science/ML/AI (@datascience_bds) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 13 667 مشترک است و جایگاه 9 391 را در دسته فناوری و برنامهها و رتبه 31 743 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 13 667 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 08 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 150 و در ۲۴ ساعت گذشته برابر 4 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 7.97% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.27% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 1 089 بازدید دریافت میکند. در اولین روز معمولاً 310 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 5 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند panda, learning, row, api, ethic تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“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...”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 09 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
13 667
مشترکین
+424 ساعت
+437 روز
+15030 روز
آرشیو پست ها
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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/
