متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Machine Learning
الذهاب إلى القناة على Telegram
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📈 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
أرشيف المشاركات
40 071
📌 Hallucinations in LLMs Are Not a Bug in the Data
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-16 | ⏱️ Read time: 10 min read
It’s a feature of the architecture
#DataScience #AI #Python
40 071
📌 Bayesian Thinking for People Who Hated Statistics
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-16 | ⏱️ Read time: 12 min read
You already think like a Bayesian. Your stats class just taught the formula before the…
#DataScience #AI #Python
40 071
Rocket.new lets you build a full website using prompts with their vibe solutioning platform 🧠⚡️
You describe it, it does the work.
🎁 For the first time on this channel: 100% OFF for 2 months
🛒 Coupon code:
X7K2M9P4R1NQ
✔️ Valid on all pricing plans
Go to Rocket.new now, enter the code, claim your 2 months free, or miss out and come back later paying the full subscription. 💸
claim your 2 months free40 071
📌 The Causal Inference Playbook: Advanced Methods Every Data Scientist Should Master
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-15 | ⏱️ Read time: 17 min read
Master six advanced causal inference methods with Python: doubly robust estimation, instrumental variables, regression discontinuity,…
#DataScience #AI #Python
40 071
📌 The 2026 Data Mandate: Is Your Governance Architecture a Fortress or a Liability?
🗂 Category: DATA GOVERNANCE
🕒 Date: 2026-03-15 | ⏱️ Read time: 8 min read
Is your data strategy 2026-ready? Get a deep dive into the mandatory shift toward human-in-the-loop…
#DataScience #AI #Python
40 071
📌 The Current Status of The Quantum Software Stack
🗂 Category: QUANTUM COMPUTING
🕒 Date: 2026-03-14 | ⏱️ Read time: 8 min read
How do we program quantum computers today?
#DataScience #AI #Python
40 071
📌 The Multi-Agent Trap
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-14 | ⏱️ Read time: 12 min read
Google DeepMind found multi-agent networks amplify errors 17x. Learn 3 architecture patterns that separate $60M…
#DataScience #AI #Python
40 071
📌 Personalized Restaurant Ranking with a Two-Tower Embedding Variant
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-03-13 | ⏱️ Read time: 6 min read
How a lightweight two-tower model improved restaurant discovery when popularity ranking failed
#DataScience #AI #Python
40 071
📌 How Vision Language Models Are Trained from “Scratch”
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-13 | ⏱️ Read time: 13 min read
A deep dive into exactly how text-only language models are finetuned to see images
#DataScience #AI #Python
40 071
📌 Why Care About Prompt Caching in LLMs?
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-13 | ⏱️ Read time: 11 min read
Optimizing the cost and latency of your LLM calls with Prompt Caching
#DataScience #AI #Python
40 071
Repost from Machine Learning with Python
🗂 Building our own mini-Skynet — a collection of 10 powerful AI repositories from big tech companies
1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.
2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".
3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.
4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.
5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.
6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.
If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit.
tags: #github #LLM #AI #ML
➡️ https://t.me/CodeProgrammer
40 071
📌 How to Build Agentic RAG with Hybrid Search
🗂 Category: RAG
🕒 Date: 2026-03-13 | ⏱️ Read time: 7 min read
Learn how to build a powerful agentic RAG system
#DataScience #AI #Python
40 071
📌 A Tale of Two Variances: Why NumPy and Pandas Give Different Answers
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-13 | ⏱️ Read time: 7 min read
Imagine you are analyzing a small dataset: You want to calculate some summary statistics to…
#DataScience #AI #Python
40 071
📌 I Finally Built My First AI App (And It Wasn’t What I Expected)
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-12 | ⏱️ Read time: 14 min read
A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure
#DataScience #AI #Python
40 071
📌 Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-03-12 | ⏱️ Read time: 11 min read
Navigating the performance cliff: How pairing MRL with int8 and binary quantization balances infrastructure costs…
#DataScience #AI #Python
40 071
📌 Solving the Human Training Data Problem
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-12 | ⏱️ Read time: 18 min read
How AI has completely transformed the way I study as a graduate student
#DataScience #AI #Python
40 071
Repost from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
40 071
Repost from Machine Learning with Python
Machine Learning in Python (Course Notes)
I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!
Here’s what you’ll learn:
🔘 Linear Regression - The foundation of predictive modeling
🔘 Logistic Regression - Predicting probabilities and classifications
🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data
🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master
🔘 OLS, R-squared, F-test - Key metrics to evaluate your models
https://t.me/CodeProgrammer || Share 🌐 and Like 👍
40 071
📌 Exploratory Data Analysis for Credit Scoring with Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-12 | ⏱️ Read time: 16 min read
Understanding default risk through statistical analysis of borrower and loan characteristics.
#DataScience #AI #Python
