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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 40 208 подписчиков, занимая 3 344 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 40 208 подписчиков.

Согласно последним данным от 03 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 338, а за последние 24 часа — 9, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.04%. В первые 24 часа после публикации контент обычно набирает 2.42% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 822 просмотров. В течение первых суток публикация набирает 973 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 04 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.

40 208
Подписчики
+924 часа
+727 дней
+33830 день
Архив постов
📌 AI Agents: The Intersection of Tool Calling and Reasoning in Generative AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2
📌 AI Agents: The Intersection of Tool Calling and Reasoning in Generative AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-05 | ⏱️ Read time: 13 min read Unpacking problem solving and tool-driven decision making in AI

📌 How I Turned IPL Stats into a Mesmerizing Bar Chart Race 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 8 m
📌 How I Turned IPL Stats into a Mesmerizing Bar Chart Race 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 8 min read A step-by-step guide to creating captivating animated visualizations for data storytelling

📌 The Rise of Pallas: Unlocking TPU Potential with Custom Kernels 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-06 |
📌 The Rise of Pallas: Unlocking TPU Potential with Custom Kernels 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 17 min read Accelerating AI/ML Model Training with Custom Operators – Part 3

📌 FormulaFeatures: A Tool to Generate Highly Predictive Features for Interpretable Models 🗂 Category: 🕒 Date: 2024-10-06 |
📌 FormulaFeatures: A Tool to Generate Highly Predictive Features for Interpretable Models 🗂 Category: 🕒 Date: 2024-10-06 | ⏱️ Read time: 41 min read Create more interpretable models by using concise, highly predictive features, automatically engineered based on arithmetic…

📌 Exploring the AI Alignment Problem with GridWorlds 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-06 | ⏱️ Read time
📌 Exploring the AI Alignment Problem with GridWorlds 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 25 min read It’s difficult to build capable AI agents without encountering orthogonal goals

📌 How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs? 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-
📌 How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs? 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-06 | ⏱️ Read time: 15 min read Delve into an end-to-end Machine Learning project to improve the quality of the Open Food…

📌 Getting Started with Powerful Data Tables in your Python Web Apps 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read
📌 Getting Started with Powerful Data Tables in your Python Web Apps 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 6 min read Using AG Grid to build a Finance app in pure Python with Reflex

📌 Top 5 Geospatial Data APIs for Advanced Analysis 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 22 min read
📌 Top 5 Geospatial Data APIs for Advanced Analysis 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-06 | ⏱️ Read time: 22 min read Explore Overpass, Geoapify, Distancematrix.ai, Amadeus, and Mapillary for Advanced Mapping and Location Data

📌 Arrays – Data Structures & Algorithms for Data Scientists 🗂 Category: CODING 🕒 Date: 2024-10-07 | ⏱️ Read time: 6 min re
📌 Arrays – Data Structures & Algorithms for Data Scientists 🗂 Category: CODING 🕒 Date: 2024-10-07 | ⏱️ Read time: 6 min read How dynamic and static arrays work under the hood

📌 Discover AWS Lambda Basics to Run Powerful Serverless Functions 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-07 |
📌 Discover AWS Lambda Basics to Run Powerful Serverless Functions 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-07 | ⏱️ Read time: 12 min read Learn how I navigated setting up AWS Lambda for the first time

📌 AlphaFold 2 Through the Context of BERT 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 9 min read Understanding AI appli
📌 AlphaFold 2 Through the Context of BERT 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 9 min read Understanding AI applications in bio for machine learning engineers

📌 Using Linear Equations + LLM to Solve LinkedIn Queens Game 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 11 min read Pr
📌 Using Linear Equations + LLM to Solve LinkedIn Queens Game 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 11 min read Prompting GPT to form and solve the linear equations using PuLP

📌 Scaling RAG from POC to Production 🗂 Category: CHATGPT 🕒 Date: 2024-10-07 | ⏱️ Read time: 8 min read Common challenges a
📌 Scaling RAG from POC to Production 🗂 Category: CHATGPT 🕒 Date: 2024-10-07 | ⏱️ Read time: 8 min read Common challenges and architectural components to enable scaling

📌 K Nearest Neighbor Regressor, Explained: A Visual Guide with Code Examples 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-07 |
📌 K Nearest Neighbor Regressor, Explained: A Visual Guide with Code Examples 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-07 | ⏱️ Read time: 11 min read Finding the neighbors FAST with KD Trees and Ball Trees

📌 Supercharge Your LLM Apps using DSPy and Langfuse 🗂 Category: NATURAL LANGUAGE PROCESSING 🕒 Date: 2024-10-07 | ⏱️ Read t
📌 Supercharge Your LLM Apps using DSPy and Langfuse 🗂 Category: NATURAL LANGUAGE PROCESSING 🕒 Date: 2024-10-07 | ⏱️ Read time: 14 min read Build Production Grade LLM Apps with Ease

📌 Implementing Sequential Algorithms on TPU 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 13 min read Accelerating AI/ML
📌 Implementing Sequential Algorithms on TPU 🗂 Category: 🕒 Date: 2024-10-07 | ⏱️ Read time: 13 min read Accelerating AI/ML Model Training with Custom Operators – Part 3.A

📌 How to Talk About Data and Analysis Simply 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 21 min read So that it is unde
📌 How to Talk About Data and Analysis Simply 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 21 min read So that it is understandable and engaging to (almost) everyone

📌 Pandora’s Cloud Migration: Conquer the 7 “Bringers of Evil” 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 20 min read A
📌 Pandora’s Cloud Migration: Conquer the 7 “Bringers of Evil” 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 20 min read A guide to conquering cloud migration challenges

📌 Adding Gradient Backgrounds to Plotly Charts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-08 | ⏱️ Read time: 5 min read Usin
📌 Adding Gradient Backgrounds to Plotly Charts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-08 | ⏱️ Read time: 5 min read Using Plotly rectangle shapes to improve data visualisation

📌 Precisely Compare Geographical Regions with GeoPandas 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 9 min read Filling
📌 Precisely Compare Geographical Regions with GeoPandas 🗂 Category: 🕒 Date: 2024-10-08 | ⏱️ Read time: 9 min read Filling maps with area measurements