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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

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📈 Аналитический обзор Telegram-канала Machine Learning

Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 205 подписчиков, занимая 3 352 место в категории Технологии и приложения и 228 место в регионе Сирия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 205 подписчиков.

Согласно последним данным от 02 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 343, а за последние 24 часа — 10, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 1.99%. В первые 24 часа после публикации контент обычно набирает 2.28% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 800 просмотров. В течение первых суток публикация набирает 915 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
  • Тематические интересы: Контент сосредоточен на ключевых темах, таких как distance, insidead, gpu, learning, degree.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Благодаря высокой частоте обновлений (последние данные получены 03 июля, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

40 205
Подписчики
+1024 часа
+837 дней
+34330 день
Архив постов
📌 Data Visualization Explained (Part 2): An Introduction to Visual Variables 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-1
📌 Data Visualization Explained (Part 2): An Introduction to Visual Variables 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-10-01 | ⏱️ Read time: 7 min read A non-technical and accessible guide to the underlying concept behind visual design: visual encoding channels

📌 How to Improve the Efficiency of Your PyTorch Training Loop 🗂 Category: DEEP LEARNING 🕒 Date: 2025-10-01 | ⏱️ Read time:
📌 How to Improve the Efficiency of Your PyTorch Training Loop 🗂 Category: DEEP LEARNING 🕒 Date: 2025-10-01 | ⏱️ Read time: 14 min read Learn how to diagnose and resolve bottlenecks in PyTorch using the numworkers, pinmemory, and profiler…

📌 Are Foundation Models Ready for Your Production Tabular Data? 🗂 Category: LARGE DATA MODELS 🕒 Date: 2025-10-01 | ⏱️ Read
📌 Are Foundation Models Ready for Your Production Tabular Data? 🗂 Category: LARGE DATA MODELS 🕒 Date: 2025-10-01 | ⏱️ Read time: 15 min read A complete review of architectures to make zero-shot predictions in the most common types of…

🌍 Work Abroad for Skilled Construction Workers! Salary: $450–700 per month ✅ Free accommodation ✅ Free meals ✅ Official 1-ye
🌍 Work Abroad for Skilled Construction Workers! Salary: $450–700 per month ✅ Free accommodation ✅ Free meals ✅ Official 1-year work contract 📌 Open positions: • Tilers • Painters / Plasterers • Bricklayers • Facade Workers • Plumbers • Electricians 💡 Experience required! 📲 Apply now #ad InsideAds

“I turned $1,000 into $4,500 in just 2 weeks — but nobody believed me until they saw my account.” Want to know the exact sign
“I turned $1,000 into $4,500 in just 2 weeks — but nobody believed me until they saw my account.” Want to know the exact signals I used? The secret’s hidden right here — but hurry, only a few will see this in time. #ad InsideAds

📌 The Data Strategy Choice Cascade 🗂 Category: 🕒 Date: 2024-09-16 | ⏱️ Read time: 23 min read What your data strategy shou
📌 The Data Strategy Choice Cascade 🗂 Category: 🕒 Date: 2024-09-16 | ⏱️ Read time: 23 min read What your data strategy should look like

📌 How to Implement State-of-the-Art Masked AutoEncoders (MAE) 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time:
📌 How to Implement State-of-the-Art Masked AutoEncoders (MAE) 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 8 min read A Step-by-Step Guide to Building MAE with Vision Transformers

📌 Unit Disk Uniform Sampling 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 15 min read Discover the optimal
📌 Unit Disk Uniform Sampling 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 15 min read Discover the optimal transformations to apply on the standard 0,1 uniform random generator for uniformly…

📌 Vision Mamba: Like a Vision Transformer but Better 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 26 min re
📌 Vision Mamba: Like a Vision Transformer but Better 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 26 min read Part 4 – Towards Mamba State Space Models for Images, Videos and Time Series

📌 Teaching Your Model to Learn from Itself 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 6 min read In machi
📌 Teaching Your Model to Learn from Itself 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 6 min read In machine learning, more data leads to better results. But labeling data can be expensive…

📌 Disability, Accessibility, and AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 11 min read A d
📌 Disability, Accessibility, and AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 11 min read A discussion of how AI can help and harm people with disabilities

📌 Introducing NumPy, Part 4: Doing Math with Arrays 🗂 Category: 🕒 Date: 2024-09-16 | ⏱️ Read time: 12 min read Plus readin
📌 Introducing NumPy, Part 4: Doing Math with Arrays 🗂 Category: 🕒 Date: 2024-09-16 | ⏱️ Read time: 12 min read Plus reading and writing array data!

📌 PySpark Explained: The InferSchema Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 10 min read Think
📌 PySpark Explained: The InferSchema Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 10 min read Think before using this common option when reading large CSV’s

“Nobody believed you could grow small capital—until I saw this.” $1,000 turned into real profit before my eyes. The secret? B
“Nobody believed you could grow small capital—until I saw this.” $1,000 turned into real profit before my eyes. The secret? Bonus fuel & copytrading with Elite Gold. Want proof? See how it’s actually done before the bonus ends. #ad InsideAds

📌 Football and Geometry – Passing Networks 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 12 min read Analyzi
📌 Football and Geometry – Passing Networks 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 12 min read Analyzing Bayer Leverkusen’s Passing Networks from Last Season

📌 Model Management with MLflow, Azure, and Docker 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-17 | ⏱️ Read time: 11 min r
📌 Model Management with MLflow, Azure, and Docker 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-17 | ⏱️ Read time: 11 min read A guide to tracking experiments and managing models

📌 The Math Behind Kernel Density Estimation 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 13 min read Explor
📌 The Math Behind Kernel Density Estimation 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 13 min read Exploring the foundations, concepts, and math of kernel density estimation

📌 Polars + NVIDIA GPU Tutorial 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 4 min read Using Polars with NV
📌 Polars + NVIDIA GPU Tutorial 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 4 min read Using Polars with NVIDIA GPU can speed up your data pipelines

📌 GPU Accelerated Polars – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-17 |
📌 GPU Accelerated Polars – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 16 min read Fast Dataframes for Big Problems

Today I am 3️⃣0️⃣ years old, I am excited to make more successes and achievements My previous year was full of exciting events and economic, political and programmatic noise, but I kept moving forward Best regards Eng. @HusseinSheikho 🔤