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Data science/ML/AI

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
Programming, data science, ML - free courses by Big Data Specialistالشبكة:Programming, data science, ML - free courses by Big Data Specialistالهند31 693التكنولوجيات والتطبيقات9 381

📈 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 9 381 في فئة التكنولوجيات والتطبيقات والمرتبة 31 693 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.

13 667
المشتركون
+424 ساعات
+437 أيام
+15030 أيام
أرشيف المشاركات
Data science/ML/AI
Data science/ML/AI
13 667
R Cheatsheet - Part 3
R Cheatsheet - Part 3

Data science/ML/AI
Data science/ML/AI
13 667
Projects To Learn AI and LLM Engineering
Projects To Learn AI and LLM Engineering

Data science/ML/AI
Data science/ML/AI
13 667
PCA Dimensionality Reduction Cheatsheet
PCA Dimensionality Reduction Cheatsheet

Data science/ML/AI
Data science/ML/AI
13 667
R Cheatsheet - Part 2
R Cheatsheet - Part 2

Data science/ML/AI
Data science/ML/AI
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The Curse of Dimensionality 🧩 Here’s something that trips up many beginners: More features ≠ always better. When your dataset has too many features (dimensions), weird things happen: ⛔️ Distances between points become meaningless. ⛔️ Models struggle to generalize. ⛔️Training time explodes. 👉 Solution: techniques like PCA, feature selection, or just collecting smarter data instead of more data. Remember: Adding noise isn’t adding information.

Data science/ML/AI
Data science/ML/AI
13 667
R CHEATSHEET - Part 1
R CHEATSHEET - Part 1

Data science/ML/AI
Data science/ML/AI
13 667
Data Structure
Data Structure

Data science/ML/AI
Data science/ML/AI
13 667
SQL for Data Science 📈.pdf2.25 KB

Data science/ML/AI
Data science/ML/AI
13 667
Overfitting vs Underfitting 🎯 Why do ML models fail? Usually because of one of these two villains: Overfitting: The model me
Overfitting vs Underfitting 🎯 Why do ML models fail? Usually because of one of these two villains: Overfitting: The model memorizes training data but fails on new data. (Like a student who memorizes past exam questions but can’t handle a new one.) Underfitting: The model is too simple to capture patterns. (Like using a straight line to fit a curve.) The sweet spot? A model that generalizes well. Note: Regularization, cross-validation, and more data usually help fight these problems.

Data science/ML/AI
Data science/ML/AI
13 667
AI vs ML vs Deep Learning 🤖 You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not. AI (A
AI vs ML vs Deep Learning 🤖 You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not. AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning. ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed. Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc. Think of it this way: AI = Science ML = A chapter in the science Deep Learning = A paragraph in that chapter.

Data science/ML/AI
Data science/ML/AI
13 667
Mathematical Foundations For Deep Learning
Mathematical Foundations For Deep Learning

Data science/ML/AI
Data science/ML/AI
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Neural Networks and Deep Learning by Michael Nielsen.pdf5.82 MB

Data science/ML/AI
Data science/ML/AI
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photo content

Data science/ML/AI
Data science/ML/AI
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🔥 Building models is fun… but here’s the real test: is your model actually any good, or just pretending? 👀 Topic:  Evals in Data Science  Evaluations—or evals—are our model’s report card. They tell us: - For a spam filter: Do we catch all spam (recall) without misclassifying grandma’s emails as junk (precision)? - For price prediction: How close are our predictions on average (RMSE)? But evals aren’t just about numbers—they influence trust, fairness, and real-world usefulness of our models. Discussion prompts: - What’s your go-to evaluation metric and why? - Seen a model that looked great on paper but flopped in reality? - Should fairness & usability be considered first-class evaluation metrics alongside accuracy? Free book to dive deeper: - Fairness and Machine Learning — rigorous, practical guide to evaluating models for fairness: https://fairmlbook.org/ Drop your thoughts below ⬇️

Data science/ML/AI
Data science/ML/AI
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photo content

Data science/ML/AI
Data science/ML/AI
13 667
photo content

Data science/ML/AI
Data science/ML/AI
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TOP ML Interview Problems
+5
TOP ML Interview Problems

Data science/ML/AI
Data science/ML/AI
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Machine_Learning_For_Dummies_by_John_Paul_Mueller,_Luca_Massaron.pdf11.81 MB

Data science/ML/AI
Data science/ML/AI
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photo content

Data science/ML/AI
Data science/ML/AI
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Jupyter Notebook Basics.pdf7.43 KB