متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Data science/ML/AI
الذهاب إلى القناة على Telegram
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
+437 أيام
+15030 أيام
أرشيف المشاركات
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Hey everyone 👋
Tomorrow we are kicking off a new short & free series called:
📊 Data Importing Series 📊
We’ll go through all the real ways to pull data into Python:
→ CSV, Excel, JSON and more
→ Databases & SQL databases
→ APIs, Google Sheets, even PDFs & web scraping
Short lessons, ready-to-copy code, zero boring theory.
First part drops tomorrow.
Turn on notifications so you don’t miss it 🔔
Who’s excited? React with a 🔥 if you are.
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Normalization vs Standardization: Why They’re Not the Same
People treat these two as interchangeable. they’re not.
👉 Normalization (Min-Max scaling):
Compresses values to 0–1.
Useful when magnitude matters (pixel values, distances).
👉 Standardization (Z-score):
Centers data around mean=0, std=1.
Useful when distribution shape matters (linear/logistic regression, PCA).
🔑 Key idea:
Normalization preserves relative proportions.
Standardization preserves statistical structure.
Pick the wrong one, and your model’s geometry becomes distorted.
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📚 Data Science Riddle - CNN Kernels
Which convolution increases channel depth but not spatial size?
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✅ Complete AI (Artificial Intelligence) Roadmap 🤖🚀
1️⃣ Basics of AI
🔹 What is AI?
🔹 Types: Narrow AI vs General AI
🔹 AI vs ML vs DL
🔹 Real-world applications
2️⃣ Python for AI
🔹 Python syntax & libraries
🔹 NumPy, Pandas for data handling
🔹 Matplotlib, Seaborn for visualization
3️⃣ Math Foundation
🔹 Linear Algebra: Vectors, Matrices
🔹 Probability & Statistics
🔹 Calculus basics
🔹 Optimization techniques
4️⃣ Machine Learning (ML)
🔹 Supervised vs Unsupervised
🔹 Regression, Classification, Clustering
🔹 Scikit-learn for ML
🔹 Model evaluation metrics
5️⃣ Deep Learning (DL)
🔹 Neural Networks basics
🔹 Activation functions, backpropagation
🔹 TensorFlow / PyTorch
🔹 CNNs, RNNs, LSTMs
6️⃣ NLP (Natural Language Processing)
🔹 Text cleaning & tokenization
🔹 Word embeddings (Word2Vec, GloVe)
🔹 Transformers & BERT
🔹 Chatbots & summarization
7️⃣ Computer Vision
🔹 Image processing basics
🔹 OpenCV for CV tasks
🔹 Object detection, image classification
🔹 CNN architectures (ResNet, YOLO)
8️⃣ Model Deployment
🔹 Streamlit / Flask APIs
🔹 Docker for containerization
🔹 Deploy on cloud: Render, Hugging Face, AWS
9️⃣ Tools & Ecosystem
🔹 Git & GitHub
🔹 Jupyter Notebooks
🔹 DVC, MLflow (for tracking models)
🔟 Build AI Projects
🔹 Chatbot, Face recognition
🔹 Spam classifier, Stock prediction
🔹 Language translator, Object detector
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📚 Data Science Riddle
Your model's loss fluctuates but doesn't decrease overall. What's the most likely issue?
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The Difference Between Model Accuracy and Business Accuracy
A model can be 95% accurate…
yet deliver 0% business value.
Why❔
Because data science metrics ≠ business metrics.
📌 Examples:
- A fraud model catches tiny fraud but misses large ones
- A churn model predicts already obvious churners
- A recommendation model boosts clicks but reduces revenue
Always align ML metrics with business KPIs.
Otherwise, your “great model” is just a great illusion.
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📚 Data Science Riddle
Your estimate has high variance. Best fix?
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Covers Spark for ML, graph processing (GraphFrames), and integration with Hadoop from Stanford University.
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📚 Data Science Riddle
A feature has low importance but domain experts insist it matters. What do you do?
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📚 Data Science Riddle
You need fast reads of small files. What storage options fits best?
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🛠️ Running Code in Jupyter Notebooks
Jupyter Notebooks let you write & run code interactively.
Here’s a quick guide to make your workflow smoother:
▶️ Kernel & Code Cells
- Each notebook is tied to a single kernel (e.g. IPython).
- Code cells are where you write and execute code.
⌨️ Useful Shortcuts
- Shift + Enter → run current cell, move to next
- Alt + Enter → run current cell, insert new one below
- Ctrl + Enter → run current cell, stay in place
🔄 Kernel Management
- Interrupt the kernel if code hangs.
- Restart kernel to reset memory & variables.
🖥️ Output Handling
- Results & errors appear directly under the cell.
- Long-running code outputs appear as they’re generated.
- Large outputs can be scrolled or collapsed for clarity.
💡 Pro Tip:
Always “Restart & Run All” before sharing or saving a notebook.
This ensures reproducibility and clean results.
👉 Explore
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📚 Data Science Riddle
You want to prevent inconsistent data across environments. What helps most?
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The Big Data bible from Stanford: MapReduce, Spark, recommendation systems, PageRank, locality-sensitive hashing, Large scale machine learning and mining social networks/streams all explained clearly with real algorithms you can code today. 500 pages of pure gold.
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📚 Data Science Riddle
You want to detect extreme values visually in one plot. Which one is best?
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📚 Data Science Riddle
A query runs slowly due to large table scans. What's the most targeted fix?
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📚 Data Science Riddle
Two team members run the same notebook but get different results. What's the culprit?
