Machine Learning

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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

إظهار المزيد

الشبكة:Machine Learning with Python سوريا232 التكنولوجيات والتطبيقات3 398...

📈 نظرة تحليلية على قناة تيليجرام Machine Learning

تُعد قناة Machine Learning (@machinelearning9) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 40 100 مشتركاً، محتلاً المرتبة 3 398 في فئة التكنولوجيات والتطبيقات والمرتبة 232 في منطقة سوريا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 40 100 مشتركاً.

بحسب آخر البيانات بتاريخ 23 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 379، وفي آخر 24 ساعة بمقدار 30، مع بقاء الوصول العام مرتفعاً.

حالة التحقق: غير موثّقة
معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 1.92‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.16‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
وصول المنشورات: يحصل كل منشور على متوسط 770 مشاهدة. وخلال اليوم الأول يجمع عادةً 466 مشاهدة.
التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 3.
الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل distance, insidead, gpu, learning, degree.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 24 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

40 100

المشتركون

+3024 ساعات

+337 أيام

+37930 أيام

770

عرض المشاهدات

~ 46624 ساعات

~ 62048 ساعات

1.92%

معدل المشاركة

~ 3

المشاركات في اليوم

Ads index

beta

أرشيف المشاركات

40 106

Repost from Kaggle Data Hub

📊 Data Science Cheat Sheets 📦 596.3 MB | 👍 5.5K | ⬇️ 73.4K 📡 @DATASETS1

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Repost from Machine Learning with Python

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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📌 Turning 127 Million Data Points Into an Industry Report 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-31 | ⏱️ Read time: 7 min read What I learned about data wrangling, segmentation, and storytelling while building an application security report… #DataScience #AI #Python

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📌 Building a Personal AI Agent in a couple of Hours 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-31 | ⏱️ Read time: 16 min read I’ve been so surprised by how fast individual builders can now ship real and useful… #DataScience #AI #Python

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📌 Why Data Scientists Should Care About Quantum Computing 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2026-03-30 | ⏱️ Read time: 6 min read Sara A. Metwalli on the rise of a promising new technology, the effects of LLM… #DataScience #AI #Python

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📌 How to Lie with Statistics with your Robot Best Friend 🗂 Category: SCIENCE 🕒 Date: 2026-03-30 | ⏱️ Read time: 12 min read What is p hacking, is it bad, and can you get ai to do it… #DataScience #AI #Python

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📌 Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-30 | ⏱️ Read time: 16 min read SHAP needs 30 ms to explain a fraud prediction. That explanation is stochastic, runs after… #DataScience #AI #Python

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📌 How to Become an AI Engineer Fast (Skills, Projects, Salary) 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-29 | ⏱️ Read time: 12 min read Spoiler, it will take longer than 3 months #DataScience #AI #Python

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📌 Self-Healing Neural Networks in PyTorch: Fix Model Drift in Real Time Without Retraining 🗂 Category: DEEP LEARNING 🕒 Date: 2026-03-29 | ⏱️ Read time: 22 min read What happens when your production model drifts and retraining isn’t an option? This article shows… #DataScience #AI #Python

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📌 Using OpenClaw as a Force Multiplier: What One Person Can Ship with Autonomous Agents 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-28 | ⏱️ Read time: 25 min read It’s easier than ever to 10x your output with agentic AI. #DataScience #AI #Python

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📌 From NetCDF to Insights: A Practical Pipeline for City-Level Climate Risk Analysis 🗂 Category: CLIMATE CHANGE 🕒 Date: 2026-03-28 | ⏱️ Read time: 7 min read Integrating CMIP6 projections, ERA5 reanalysis, and impact models into a lightweight, interpretable workflow #DataScience #AI #Python

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📌 How ElevenLabs Voice AI Is Replacing Screens in Warehouse and Manufacturing Operations 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-27 | ⏱️ Read time: 10 min read A warehouse picking operation is the process of collecting items from storage locations to fulfil… #DataScience #AI #Python

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📌 A Beginner’s Guide to Quantum Computing with Python 🗂 Category: QUANTUM COMPUTING 🕒 Date: 2026-03-27 | ⏱️ Read time: 7 min read Simulate a quantum computer with Qiskit #DataScience #AI #Python

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📌 Building a Production-Grade Multi-Node Training Pipeline with PyTorch DDP 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-27 | ⏱️ Read time: 14 min read A practical, code-driven guide to scaling deep learning across machines — from NCCL process groups… #DataScience #AI #Python

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Repost from Machine Learning with Python

Classical filters & convolution: The heart of computer vision Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them. More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution

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📌 What the Bits-over-Random Metric Changed in How I Think About RAG and Agents 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-26 | ⏱️ Read time: 19 min read Why retrieval that looks excellent on paper can still behave like noise in real RAG… #DataScience #AI #Python

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📌 Beyond Code Generation: AI for the Full Data Science Workflow 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-26 | ⏱️ Read time: 10 min read Using Codex and MCP to connect Google Drive, GitHub, BigQuery, and analysis in one real workflow #DataScience #AI #Python

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📌 How to Make Your AI App Faster and More Interactive with Response Streaming 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-26 | ⏱️ Read time: 8 min read In my latest posts, we’ve talked a lot about prompt caching as well as caching… #DataScience #AI #Python

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📌 My Models Failed. That’s How I Became a Better Data Scientist. 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-25 | ⏱️ Read time: 9 min read Data Leakage, Real-World Models, and the Path to Production AI in Healthcare #DataScience #AI #Python