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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

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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
📌 Graph RAG into Production – step-by-step 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-23 | ⏱️ Read time: 17 min r
📌 Graph RAG into Production – step-by-step 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-23 | ⏱️ Read time: 17 min read A GCP native, fully serverless implementation that you will replicate in minutes

📌 Semantic Layer for the People and by the People 🗂 Category: 🕒 Date: 2024-09-23 | ⏱️ Read time: 14 min read My 3 +1 joker
📌 Semantic Layer for the People and by the People 🗂 Category: 🕒 Date: 2024-09-23 | ⏱️ Read time: 14 min read My 3 +1 jokers with templates for building a powerful analytical semantic layer

📌 Zero-Shot Localization with CLIP-Style Encoders 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-24 | ⏱️ Read time: 1
📌 Zero-Shot Localization with CLIP-Style Encoders 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-24 | ⏱️ Read time: 11 min read How can we see what a vision encoder sees?

📌 A Deep Dive into Odds Ratio 🗂 Category: STATISTICS 🕒 Date: 2024-09-24 | ⏱️ Read time: 20 min read Understanding, calcula
📌 A Deep Dive into Odds Ratio 🗂 Category: STATISTICS 🕒 Date: 2024-09-24 | ⏱️ Read time: 20 min read Understanding, calculating, visualizing, and interpreting odds ratios and their confidence intervals with practical examples in…

📌 Building an Interactive UI for Llamaindex Workflows 🗂 Category: 🕒 Date: 2024-09-24 | ⏱️ Read time: 11 min read A guide t
📌 Building an Interactive UI for Llamaindex Workflows 🗂 Category: 🕒 Date: 2024-09-24 | ⏱️ Read time: 11 min read A guide to integrating human-in-the-loop interactions using Llamaindex, FastAPI, and Streamlit

📌 Feature Engineering Techniques for Numerical Variables in Python 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-24 | ⏱️ Re
📌 Feature Engineering Techniques for Numerical Variables in Python 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-24 | ⏱️ Read time: 21 min read Learn the most useful feature engineering techniques to convert numerical values ​​into useful information for…

📌 I’ve hired 3 cohorts of data science interns – here’s my advice on getting an offer 🗂 Category: DATA SCIENCE 🕒 Date: 202
📌 I’ve hired 3 cohorts of data science interns – here’s my advice on getting an offer 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-24 | ⏱️ Read time: 16 min read Resume and interview tips for landing a data science internship

📌 Doctors Leverage Multimodal Data; Medical AI Should Too 🗂 Category: 🕒 Date: 2024-09-25 | ⏱️ Read time: 11 min read Integ
📌 Doctors Leverage Multimodal Data; Medical AI Should Too 🗂 Category: 🕒 Date: 2024-09-25 | ⏱️ Read time: 11 min read Integrating multimodal data enables a new generation of medical AI systems to better capture doctor’s…

📌 Water Cooler Small Talk: The Birthday Paradox 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-25 | ⏱️ Read time: 9 min read A l
📌 Water Cooler Small Talk: The Birthday Paradox 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-25 | ⏱️ Read time: 9 min read A look at the counterintuitive mathematics of shared birthdays

📌 Convenient Time Series Forecasting with sktime 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-25 | ⏱️ Read time: 8 min rea
📌 Convenient Time Series Forecasting with sktime 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-25 | ⏱️ Read time: 8 min read How to make forecasting as easy as a walk in the park

📌 Exposing Jailbreak Vulnerabilities in LLM Applications with ARTKIT 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-25 | ⏱️
📌 Exposing Jailbreak Vulnerabilities in LLM Applications with ARTKIT 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-25 | ⏱️ Read time: 10 min read Automated prompt-based testing to extract hidden passwords in the popular Gandalf challenge

📌 How Cohort Analysis Can Transform Your Customer Insights 🗂 Category: 🕒 Date: 2024-09-25 | ⏱️ Read time: 6 min read Disco
📌 How Cohort Analysis Can Transform Your Customer Insights 🗂 Category: 🕒 Date: 2024-09-25 | ⏱️ Read time: 6 min read Discover how tracking customer behavior over time with cohort analysis can improve engagement and retention…

📌 I Spent My Money on Benchmarking LLMs on Dutch Exams So You Don’t Have To 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024
📌 I Spent My Money on Benchmarking LLMs on Dutch Exams So You Don’t Have To 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-25 | ⏱️ Read time: 12 min read OpenAI’s new o1-preview is way too expensive for how it performs on the results

📌 VisionTS: Building Superior Forecasting Models from Images 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-26 | ⏱️ R
📌 VisionTS: Building Superior Forecasting Models from Images 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 9 min read Leveraging the power of images for time-series forecasting

📌 Simulate the Challenges of a Circular Economy for Fashion Retail 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read t
📌 Simulate the Challenges of a Circular Economy for Fashion Retail 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 17 min read Use data analytics to simulate a circular rental model for fashion retail and understand store…

📌 MCP in Practice 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-29 | ⏱️ Read time: 14 min read Mapping power, concentration, and
📌 MCP in Practice 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-29 | ⏱️ Read time: 14 min read Mapping power, concentration, and usage in the emerging AI developer ecosystem

📌 I Made My AI Model 84% Smaller and It Got Better, Not Worse 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-29 | ⏱️ Re
📌 I Made My AI Model 84% Smaller and It Got Better, Not Worse 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-29 | ⏱️ Read time: 20 min read The counterintuitive approach to AI optimization that’s changing how we deploy models

📌 Preparing Video Data for Deep Learning: Introducing Vid Prepper 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-29 | ⏱️ Rea
📌 Preparing Video Data for Deep Learning: Introducing Vid Prepper 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-29 | ⏱️ Read time: 13 min read A guide to fast video data preprocessing for machine learning

📌 Dummy Regressor, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26
📌 Dummy Regressor, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-26 | ⏱️ Read time: 7 min read Naively choosing the best number for all of your prediction

📌 Working with Embeddings: Closed versus Open Source 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-26 | ⏱️ Read time: 20 mi
📌 Working with Embeddings: Closed versus Open Source 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-26 | ⏱️ Read time: 20 min read Using techniques to improve semantic search