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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 40 334 подписчиков, занимая 3 331 место в категории Технологии и приложения и 225 место в регионе Сирия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 334 подписчиков.

Согласно последним данным от 10 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 383, а за последние 24 часа — 25, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.35%. В первые 24 часа после публикации контент обычно набирает 1.95% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 948 просмотров. В течение первых суток публикация набирает 786 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 4.
  • 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 11 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.

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+1227 дней
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Архив постов
📌 How to Benchmark DeepSeek-R1 Distilled Models on GPQA Using Ollama and OpenAI’s simple-evals 🗂 Category: LARGE LANGUAGE M
📌 How to Benchmark DeepSeek-R1 Distilled Models on GPQA Using Ollama and OpenAI’s simple-evals 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-23 | ⏱️ Read time: 12 min read Set up and run the GPQA-Diamond benchmark on DeepSeek-R1’s distilled models locally to evaluate its…

📌 Exporting MLflow Experiments from Restricted HPC Systems 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-04-23 | ⏱️ Read time:
📌 Exporting MLflow Experiments from Restricted HPC Systems 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-04-23 | ⏱️ Read time: 4 min read A workaround method that bypasses direct communication

📌 Predicting the NBA Champion with Machine Learning 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-24 | ⏱️ Read time: 10 min rea
📌 Predicting the NBA Champion with Machine Learning 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-24 | ⏱️ Read time: 10 min read Building a machine learning model to predict the NBA Champion and analyze the most impactful…

📌 Choose the Right One: Evaluating Topic Models for Business Intelligence 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-04-24
📌 Choose the Right One: Evaluating Topic Models for Business Intelligence 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-04-24 | ⏱️ Read time: 11 min read Python tutorial for evaluating top-notch bigram topic models in customer email classification

📌 How to Integrate AI into Complex Workflows 🗂 Category: THE VARIABLE 🕒 Date: 2025-04-24 | ⏱️ Read time: 3 min read This w
📌 How to Integrate AI into Complex Workflows 🗂 Category: THE VARIABLE 🕒 Date: 2025-04-24 | ⏱️ Read time: 3 min read This week, we focus on the nitty-gritty details of integrating AI workflows into new contexts.

📌 Government Funding Graph RAG 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-24 | ⏱️ Read time: 19 min read Graph visualisation
📌 Government Funding Graph RAG 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-24 | ⏱️ Read time: 19 min read Graph visualisation for UK Research and Innovation (UKRI) funding, including NetworkX, PyVis and LlamaIndex graph…

📌 AWS: Deploying a FastAPI App on EC2 in Minutes 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-04-24 | ⏱️ Read time: 5 min rea
📌 AWS: Deploying a FastAPI App on EC2 in Minutes 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-04-24 | ⏱️ Read time: 5 min read From zero to EC2: easy steps to launching an AWS Instance

📌 LLM Evaluations: from Prototype to Production 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-25 | ⏱️ Read time: 30
📌 LLM Evaluations: from Prototype to Production 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-25 | ⏱️ Read time: 30 min read How to monitor the quality of your LLM product

📌 Behind the Magic: How Tensors Drive Transformers 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-25 | ⏱️ Read time: 4
📌 Behind the Magic: How Tensors Drive Transformers 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-25 | ⏱️ Read time: 4 min read The workflow Of tensors Inside Transformers

📌 A Step-By-Step Guide To Powering Your Application With LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-25 | ⏱️ Re
📌 A Step-By-Step Guide To Powering Your Application With LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-25 | ⏱️ Read time: 8 min read Explore a hands-on guide to integrating large language models into real-world apps, not just read…

📌 Hands-on Multi Agent LLM Restaurant Simulation, with Python and OpenAI 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04
📌 Hands-on Multi Agent LLM Restaurant Simulation, with Python and OpenAI 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-28 | ⏱️ Read time: 12 min read This is how I used Large Language Models Agents to simulate an end-to-end restaurant process,…

📌 Adding Training Noise To Improve Detections In Transformers 🗂 Category: DEEP LEARNING 🕒 Date: 2025-04-28 | ⏱️ Read time:
📌 Adding Training Noise To Improve Detections In Transformers 🗂 Category: DEEP LEARNING 🕒 Date: 2025-04-28 | ⏱️ Read time: 8 min read Denoising, explained

📌 When OpenAI Isn’t Always the Answer: Enterprise Risks Behind Wrapper-Based AI Agents 🗂 Category: ARTIFICIAL INTELLIGENCE
📌 When OpenAI Isn’t Always the Answer: Enterprise Risks Behind Wrapper-Based AI Agents 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-28 | ⏱️ Read time: 8 min read Data privacy, compliance, and trust gaps in today’s AI agent integrations

📌 NumExpr: The “Faster than Numpy” Library Most Data Scientists Have Never Used 🗂 Category: PROGRAMMING 🕒 Date: 2025-04-28
📌 NumExpr: The “Faster than Numpy” Library Most Data Scientists Have Never Used 🗂 Category: PROGRAMMING 🕒 Date: 2025-04-28 | ⏱️ Read time: 8 min read A comparative performance test with NumPy

📌 Struggling to Land a Data Role in 2025? These 5 Tips Will Change That 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-28 | ⏱️ R
📌 Struggling to Land a Data Role in 2025? These 5 Tips Will Change That 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-28 | ⏱️ Read time: 7 min read Your dream data job isn’t ghosting you—you just need to search smart.

📌 How to Build an AI Budget-Planning Optimizer for Your 2026 CAPEX Review: LangGraph, FastAPI, and n8n 🗂 Category: ARTIFICI
📌 How to Build an AI Budget-Planning Optimizer for Your 2026 CAPEX Review: LangGraph, FastAPI, and n8n 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-10 | ⏱️ Read time: 20 min read Email → n8n → LangGraph → FastAPI: turning budget requests into optimised CAPEX portfolios that…

📌 Why Task-Based Evaluations Matter 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-10 | ⏱️ Read time: 4 min read This
📌 Why Task-Based Evaluations Matter 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-10 | ⏱️ Read time: 4 min read This article is adapted from a lecture series I gave at Deeplearn 2025: From Prototype…

📌 When A Difference Actually Makes A Difference 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-10 | ⏱️ Read time: 10 min read Bi
📌 When A Difference Actually Makes A Difference 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-10 | ⏱️ Read time: 10 min read Bite-Sized Analytics for Business Decision-Makers (1)

📌 Fighting Back Against Attacks in Federated Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-10 | ⏱️ Read time: 8 mi
📌 Fighting Back Against Attacks in Federated Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-10 | ⏱️ Read time: 8 min read Lessons from a multi-node simulator

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