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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Сеть:Machine Learning with Python Сирия227 Технологии и приложения3 343...

📈 Аналитический обзор Telegram-канала Machine Learning

Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 244 подписчиков, занимая 3 343 место в категории Технологии и приложения и 227 место в регионе Сирия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 244 подписчиков.

Согласно последним данным от 05 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 346, а за последние 24 часа — 22, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 1.97%. В первые 24 часа после публикации контент обычно набирает 1.86% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 794 просмотров. В течение первых суток публикация набирает 749 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 06 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.

40 244

Подписчики

+2224 часа

+987 дней

+34630 день

794

Просмотры поста

~ 74924 часа

~ 98448 часов

1.97%

Коэффициент вовлеченности

~ 3

Постов в день

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Архив постов

40 244

📌 Building a Research Agent That Can Write to Google Docs (Part 1) 🗂 Category: 🕒 Date: 2024-11-20 | ⏱️ Read time: 18 min read A tool that might help with your homework

40 244

📌 Building a Research Assistant That Can Write to Google Docs (Part 2) 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-20 | ⏱️ Read time: 14 min read A tool that might help with your homework

40 244

📌 Your Data Quality Checks Are Worth Less (Than You Think) 🗂 Category: ANALYTICS 🕒 Date: 2024-11-20 | ⏱️ Read time: 6 min read How to deliver outsized value on your data quality program

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📌 Boost Your Python Code with CUDA 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-20 | ⏱️ Read time: 12 min read Target your GPU easily with Numba’s CUDA JIT

40 244

📌 How to Build a Data-Driven Customer Management System 🗂 Category: BUSINESS 🕒 Date: 2024-11-20 | ⏱️ Read time: 16 min read A High-Level Framework for Building a CBM System with Strategic Impact

40 244

📌 Is ReFT All We Needed? 🗂 Category: DEEP LEARNING 🕒 Date: 2024-11-21 | ⏱️ Read time: 6 min read Representation Fintuning – Beyond the PEFT Techniques for fine-tuning LLMs

40 244

📌 How to Easily Deploy a Local Generative Search Engine Using VerifAI 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-11-21 | ⏱️ Read time: 10 min read An open-source initiative to help you deploy generative search based on your local files and…

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📌 Getting Started with Multimodal AI, One-Hot Encoding, and Other Beginner-Friendly Guides 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-21 | ⏱️ Read time: 5 min read Our weekly selection of must-read Editors’ Picks and original features

40 244

📌 Explaining LLMs for RAG and Summarization 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-21 | ⏱️ Read time: 9 min read A fast and low-resource method using similarity-based attribution

40 244

📌 Building a Vision Inspection CNN for an Industrial Application 🗂 Category: 🕒 Date: 2024-11-21 | ⏱️ Read time: 34 min read A step-by-step approach using PyTorch

40 244

📌 Cluster While Predict: Iterative Methods for Regression and Classification 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-21 | ⏱️ Read time: 11 min read Predictive and prescriptive analytics to bridge the gap between segmentation and prediction for real-world applications

40 244

📌 Leverage Python Inheritance in ML projects 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-21 | ⏱️ Read time: 6 min read Learn how to implement coding best practices to avoid tech debts

40 244

📌 The SyncNet Research Paper, Clearly Explained 🗂 Category: DEEP LEARNING 🕒 Date: 2025-09-20 | ⏱️ Read time: 11 min read A Deep Dive into “Out of Time: Automated Lip Sync in the Wild”

40 244

📌 Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-20 | ⏱️ Read time: 12 min read A hands-on example of building a time-series anomaly detection system entirely through visualization and prompting

40 244

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40 244

📌 Graph Neural Networks: Fraud Detection and Protein Function Prediction 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-11-21 | ⏱️ Read time: 8 min read Understanding AI applications in bio for machine learning engineers

40 244

📌 ChatGPT: Two Years Later 🗂 Category: CHATGPT 🕒 Date: 2024-11-21 | ⏱️ Read time: 25 min read Tracing the impact of the generative AI revolution

40 244

📌 Dynamic Visualizations in Python 🗂 Category: 🕒 Date: 2024-11-21 | ⏱️ Read time: 8 min read How to animate plots with OpenCV and Matplotlib

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📌 Neuromorphic Computing – an Edgier, Greener AI 🗂 Category: DEEP LEARNING 🕒 Date: 2024-11-22 | ⏱️ Read time: 18 min read There are periodic proclamations of the coming neuromorphic computing revolution. While there remain substantial challenges…

40 244

📌 NuCS: A Constraint Solver for Research, Teaching, and Production Applications 🗂 Category: DATA SCIENCE 🕒 Date: 2024-11-22 | ⏱️ Read time: 6 min read Blazing-fast constraint solving in pure Python