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 13 674 مشتركاً، محتلاً المرتبة 9 377 في فئة التكنولوجيات والتطبيقات والمرتبة 31 635 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 13 674 مشتركاً.
بحسب آخر البيانات بتاريخ 09 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 155، وفي آخر 24 ساعة بمقدار 5، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 8.03%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 2.25% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 1 098 مشاهدة. وخلال اليوم الأول يجمع عادةً 308 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 10 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
+524 ساعات
+197 أيام
+15530 أيام
أرشيف المشاركات
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✅ Natural Language Processing (NLP) Basics You Should Know 🧠💬
Understanding NLP is key to working with language-based AI systems like chatbots, translators, and voice assistants.
1️⃣ What is NLP?
NLP stands for Natural Language Processing. It enables machines to understand, interpret, and respond to human language.
2️⃣ Key NLP Tasks:
- Text classification (spam detection, sentiment analysis)
- Named Entity Recognition (NER) (identifying names, places)
- Tokenization (splitting text into words/sentences)
- Part-of-speech tagging (noun, verb, etc.)
- Machine translation (English → French)
- Text summarization
- Question answering
3️⃣ Tokenization Example:
from nltk.tokenize import word_tokenize
text = "ChatGPT is awesome!"
tokens = word_tokenize(text)
print(tokens) # ['ChatGPT', 'is', 'awesome', '!']from textblob import TextBlob
TextBlob("I love AI!").sentiment # Sentiment(polarity=0.5, subjectivity=0.6)from nltk.corpus import stopwords
words = ["this", "is", "a", "test"]
filtered = [w for w in words if w not in stopwords.words("english")]
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Our platform is finally ready. 🚀
Do you remember the platform I told you we are building for you? 👀
Free learning materials, job offers, tech updates, Udemy coupons… all in one place.
After almost 3 years of building, testing, talking to many of you and improving it step by step… it’s finally in beta. ✔️
A lot of you actually participated in developing this, as backend devs, frontend devs or designers. 🧑💻That makes me insanely proud. This is truly built by us, for us. ❤️ I’m opening early access to a small group. If you want to be one of the first inside, test it, find bugs, suggest ideas, or just see what’s under the hood…join the Beta Testers Group 👉 https://t.me/+9vt9IKi6iGAxZDhk Let’s make this thing amazing. Together. 🚀
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💸 Your Model Worked… Then the Bill Hit
You ship your model.
It runs fine.
Then the cloud bill lands and suddenly ML feels very real.
⚠️ Nobody warns you about this part.
🧠 The Part Tutorials Skip
Training is a one-time cost.
Inference is forever.
Every request costs.
Every idle minute costs.
Every bad choice repeats on the bill.
Accuracy alone will not save you.
💥 Where Money Quietly Disappears
➖ GPU when CPU was enough
➖ Instances running with low traffic
➖ No profiling, just vibes
➖ Scaling for growth that is not there
It feels small until it is not.
🛠 Quick Reality Checks
Before deploying, ask:
❔What is my cost per request?
❔Do users need this latency?
❔Can the model be smaller?
❔Can I batch requests?
If you are not measuring, you are just guessing.
📌 Real Talk
A slightly worse model that is way cheaper often wins.
Cool demos impress people.
Sustainable systems keep you building.
Learn this early and your future self will be very grateful.
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✅ Computer Vision Basics You Should Know 👁️🧠
Computer Vision (CV) enables machines to see, interpret, and understand images or videos like humans do.
1️⃣ What is Computer Vision?
It’s a field of AI that trains computers to extract meaningful info from visual inputs (images/videos).
2️⃣ Common Applications:
- Facial recognition (Face ID)
- Object detection (Self-driving cars)
- OCR (Reading text from images)
- Medical imaging (X-rays, MRIs)
- Surveillance & security
- Augmented Reality (AR)
3️⃣ Key CV Tasks:
- Image classification: What’s in the image?
- Object detection: Where is the object?
- Segmentation: What pixels belong to which object?
- Pose estimation: Detect body/face positions
- Image generation & enhancement
4️⃣ Popular Libraries & Tools:
- OpenCV
- TensorFlow & Keras
- PyTorch
- Mediapipe
- YOLO (You Only Look Once)
- Detectron2
5️⃣ Image Classification Example:
from tensorflow.keras.applications import MobileNetV2
model = MobileNetV2(weights="imagenet") 13 674
✅ Top Artificial Intelligence Concepts You Should Know 🤖🧠
🔹 1. Natural Language Processing (NLP)
Use Case: Chatbots, language translation
→ Enables machines to understand and generate human language.
🔹 2. Computer Vision
Use Case: Face recognition, self-driving cars
→ Allows machines to "see" and interpret visual data.
🔹 3. Machine Learning (ML)
Use Case: Predictive analytics, spam filtering
→ AI learns patterns from data to make decisions without explicit programming.
🔹 4. Deep Learning
Use Case: Voice assistants, image recognition
→ A type of ML using neural networks with many layers for complex tasks.
🔹 5. Reinforcement Learning
Use Case: Game AI, robotics
→ AI learns by interacting with the environment and receiving feedback.
🔹 6. Generative AI
Use Case: Text, image, and music generation
→ Models like ChatGPT or DALL·E create human-like content.
🔹 7. Expert Systems
Use Case: Medical diagnosis, legal advice
→ AI systems that mimic decision-making of human experts.
🔹 8. Speech Recognition
Use Case: Voice search, virtual assistants
→ Converts spoken language into text.
🔹 9. AI Ethics
Use Case: Bias detection, fair AI systems
→ Ensures responsible and transparent AI usage.
🔹 10. Robotic Process Automation (RPA)
Use Case: Automating repetitive office tasks
→ Uses AI to handle rule-based digital tasks efficiently.
💡 Learn these concepts to understand how AI is transforming industries!
💬 Tap ❤️ for more!
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🏠🤖 Run Your Own LOCAL LLM (Beginner Friendly)
LLMs are cool, but running your own local one hits different 😎
No cloud. No API keys. No limits.
🧩 Step 1: Install Ollama
Install Ollama on your machine (works on Mac, Windows, Linux).
Once installed, open your terminal.
🚀 Step 2: Run a model
ollama run llama3.2curl http://127.0.0.1:11434/api/generate \
-H "Content-Type: application/json" \
-d '{
"model": "llama3.2",
"prompt": "Explain overfitting like I am 12",
"stream": false
}'
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✅ Deep Learning Basics You Should Know 🧠⚡
Deep Learning is a subset of machine learning that uses neural networks with many layers to learn from data — especially large, unstructured data like images, audio, and text.
1️⃣ What is Deep Learning?
It’s an approach that mimics how the human brain works by using artificial neural networks (ANNs) to recognize patterns and make decisions.
2️⃣ Common Applications:
- Image & speech recognition
- Natural Language Processing (NLP)
- Self-driving cars
- Chatbots & virtual assistants
- Language translation
- Healthcare diagnostics
3️⃣ Key Components:
- Neurons: Basic units processing data
- Layers: Input, hidden, output
- Activation functions: ReLU, Sigmoid, Softmax
- Loss function: Measures prediction error
- Optimizer: Helps model learn (e.g. Adam, SGD)
4️⃣ Neural Network Example:
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(100,)))
model.add(Dense(1, activation='sigmoid')) 13 674
The Mechanism Behind Early Stopping
Training loss always drops.
Validation loss tells you when the model begins to memorize noise.
As training continues, the network fits smaller and smaller patterns in the training set.
Some of those patterns aren’t general.
The validation curve rises when the model crosses the point where learning becomes memorization.
Key takeaway❔
Early stopping isn’t a “hack”. It is a direct detection of when your model starts overfitting.
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+1
ℹ️ Channel update
Based on your requests, we launched:
🧠 Programming Quizzes
📚 Free Programming Books
The books channel was our most popular one before, but it was removed due to copyright issues.
Because of the huge interest, we decided to bring it back, sharing free and open books.
You also requested hands-on project based learning. We are working on it! 👨💻
Thanks for the support. More coming soon 🚀
