اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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
نمایش بیشتر📈 تحلیل کانال تلگرام Machine Learning
کانال Machine Learning (@machinelearning9) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 40 057 مشترک است و جایگاه 3 402 را در دسته فناوری و برنامهها و رتبه 232 را در منطقه سوريا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 40 057 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 22 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 372 و در ۲۴ ساعت گذشته برابر 2 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 1.94% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.16% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 775 بازدید دریافت میکند. در اولین روز معمولاً 466 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 3 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند distance, insidead, gpu, learning, degree تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 23 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
40 057
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🕒 Date: 2026-03-16 | ⏱️ Read time: 10 min read
It’s a feature of the architecture
#DataScience #AI #Python
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📌 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…
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📌 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
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📌 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
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📌 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?
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🗂 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…
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📌 Personalized Restaurant Ranking with a Two-Tower Embedding Variant
🗂 Category: MACHINE LEARNING
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How a lightweight two-tower model improved restaurant discovery when popularity ranking failed
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🗂 Category: LARGE LANGUAGE MODELS
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🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-13 | ⏱️ Read time: 11 min read
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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.
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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
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🗂 Category: RAG
🕒 Date: 2026-03-13 | ⏱️ Read time: 7 min read
Learn how to build a powerful agentic RAG system
#DataScience #AI #Python
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📌 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
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🗂 Category: LARGE LANGUAGE MODELS
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A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure
#DataScience #AI #Python
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📌 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
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🗂 Category: LARGE LANGUAGE MODELS
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How AI has completely transformed the way I study as a graduate student
#DataScience #AI #Python
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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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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 👍
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📌 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
