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 40 072 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 40 072 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.

40 072

المشتركون

+3024 ساعات

+337 أيام

+37930 أيام

770

عرض المشاهدات

~ 46624 ساعات

~ 62048 ساعات

1.92%

معدل المشاركة

~ 3

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📌 Why Most A/B Tests Are Lying to You 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-11 | ⏱️ Read time: 14 min read The 4 statistical sins that invalidate most A/B tests, plus a pre-test checklist and Bayesian… #DataScience #AI #Python

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📌 Spectral Clustering Explained: How Eigenvectors Reveal Complex Cluster Structures 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-11 | ⏱️ Read time: 10 min read Understanding why spectral clustering outperforms K-means #DataScience #AI #Python

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📌 An Intuitive Guide to MCMC (Part I): The Metropolis-Hastings Algorithm 🗂 Category: MATH 🕒 Date: 2026-03-11 | ⏱️ Read time: 14 min read Tired of the AI hype? Let’s talk about the probabilistic algorithms actually driving high-end quantitative… #DataScience #AI #Python

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📌 How the Fourier Transform Converts Sound Into Frequencies 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-11 | ⏱️ Read time: 26 min read A visual, intuition-first guide to understanding what the math is really doing — from winding… #DataScience #AI #Python

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Repost from Machine Learning with Python

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📌 When Data Lies: Finding Optimal Strategies for Penalty Kicks with Game Theory 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-10 | ⏱️ Read time: 9 min read A data-driven introduction to game theory, Nash equilibrium, and strategic decision-making #DataScience #AI #Python

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📌 Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules 🗂 Category: DEEP LEARNING 🕒 Date: 2026-03-10 | ⏱️ Read time: 14 min read I really thought I was onto something big: add a couple of simple domain rules… #DataScience #AI #Python

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Repost from Machine Learning with Python

🗂 A fresh deep learning course from MIT is now publicly available A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study. The program includes modern neural network architectures, generative models, transformers, inference, and other key topics. ➡️ Link to the course tags: #Python #DataScience #DeepLearning #AI

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📌 What Are Agent Skills Beyond Claude? 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-10 | ⏱️ Read time: 6 min read How to design and implement agent skills for custom agents outside the Claude ecosystem #DataScience #AI #Python

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📌 Building a Like-for-Like solution for Stores in Power BI 🗂 Category: DATA ANALYSIS 🕒 Date: 2026-03-10 | ⏱️ Read time: 10 min read Like-for-Like (L4L) solutions are essential for comparing elements. It’s about comparing only comparable elements, in… #DataScience #AI #Python

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📌 I Stole a Wall Street Trick to Solve a Google Trends Data Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-09 | ⏱️ Read time: 14 min read A methodology for comparing Google Trends data across countries. #DataScience #AI #Python

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Repost from Learn Python Coding

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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Repost from Machine Learning with Python

🧠 Python libraries for AI agents - complexity of learning 🔥 🟢 Easy • LangChain • tool calling • agent memory • simple agents • CrewAI • agents with roles • collaboration of several agents • SmolAgents • lightweight agents • quick experiments 🟡 Medium • LangGraph • stateful workflow • agent orchestration • LlamaIndex • RAG pipelines • data indexing • knowledge agents • OpenAI Agents SDK • tool integrations • agent workflows • Strands • agent orchestration • task coordination • Semantic Kernel • skills / plugins • AI process orchestration • PydanticAI • typed LLM applications • structured agent workflows • Langroid • message exchange between agents • interaction with tools 🔴 Difficult • AutoGen • multi-agent dialogues • autonomous agent cooperation • DSPy • programmable prompting • optimization of LLM pipelines • A2A • agent-to-agent protocol • distributed agent systems https://t.me/CodeProgrammer ✅

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📌 Three OpenClaw Mistakes to Avoid and How to Fix Them 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-09 | ⏱️ Read time: 7 min read Learn how to set up OpenClaw effectively #DataScience #AI #Python

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📌 Machine Learning at Scale: Managing More Than One Model in Production 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-09 | ⏱️ Read time: 7 min read From one model to managing a massive portfolio: What 10 years in the industry taught… #DataScience #AI #Python

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📌 Write C Code Without Learning C: The Magic of PythoC 🗂 Category: PROGRAMMING 🕒 Date: 2026-03-08 | ⏱️ Read time: 9 min read Compile native, standalone applications using the Python syntax you already know. #DataScience #AI #Python

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📌 LatentVLA: Latent Reasoning Models for Autonomous Driving 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-08 | ⏱️ Read time: 8 min read What if natural language is not the best abstraction for driving? #DataScience #AI #Python

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📌 The AI Bubble Has a Data Science Escape Hatch 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-07 | ⏱️ Read time: 12 min read Five classical data science skills are becoming the scarcest resource in tech. A 90-day roadmap… #DataScience #AI #Python

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📌 Understanding Context and Contextual Retrieval in RAG 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-07 | ⏱️ Read time: 10 min read Why traditional RAG loses context and how contextual retrieval dramatically improves retrieval accuracy #DataScience #AI #Python

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📌 What Makes Quantum Machine Learning “Quantum”? 🗂 Category: QUANTUM COMPUTING 🕒 Date: 2026-03-06 | ⏱️ Read time: 8 min read And where is it today? #DataScience #AI #Python