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Machine Learning

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 40 221 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 40 221 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.

40 221
Подписчики
+924 часа
+727 дней
+33830 день
Архив постов
📌 Cognitive Prompting in LLMs 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-19 | ⏱️ Read time: 9 min read Can we teach mach
📌 Cognitive Prompting in LLMs 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-19 | ⏱️ Read time: 9 min read Can we teach machines to think like humans?

📌 The One Mindset Change That Launched Me into Data Science 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-19 | ⏱️ Read time: 13
📌 The One Mindset Change That Launched Me into Data Science 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-19 | ⏱️ Read time: 13 min read Make it happen: tiny changes to break into data science or any dream career

📌 How Much Stress Can Your Server Handle When Self-Hosting LLMs? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-19 | ⏱️ Read tim
📌 How Much Stress Can Your Server Handle When Self-Hosting LLMs? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-19 | ⏱️ Read time: 7 min read Do you need more GPUs or a modern GPU? How do you make infrastructure decisions?

📌 Understanding LLMs from Scratch Using Middle School Math 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-19 | ⏱️ Rea
📌 Understanding LLMs from Scratch Using Middle School Math 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-19 | ⏱️ Read time: 52 min read In this article, we talk about how LLMs work, from scratch – assuming only that…

📌 How to Get Started on Your Data Science Career Journey 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-20 | ⏱️ Read time: 6 mi
📌 How to Get Started on Your Data Science Career Journey 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-20 | ⏱️ Read time: 6 min read Six considerations for beginners to pick a resource for upskilling in Data Science and AI/ML

📌 AI Model Optimization on AWS Inferentia and Trainium 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-20 | ⏱️ Read ti
📌 AI Model Optimization on AWS Inferentia and Trainium 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-20 | ⏱️ Read time: 11 min read Tips for accelerating ML with AWS Neuron SDK

📌 ETL Pipelines in Python: Best Practices and Techniques 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-20 | ⏱️ Read time: 1
📌 ETL Pipelines in Python: Best Practices and Techniques 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-20 | ⏱️ Read time: 12 min read Strategies for Enhancing Generalizability, Scalability, and Maintainability in Your ETL Pipelines

📌 Introducing the AI-3P Assessment Framework: Score AI Projects Before Committing Resources 🗂 Category: ARTIFICIAL INTELLIG
📌 Introducing the AI-3P Assessment Framework: Score AI Projects Before Committing Resources 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-24 | ⏱️ Read time: 13 min read A question-driven scorecard to prioritize and de-risk AI initiatives before implementation

📌 PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks 🗂 Category: DEEP LEARNING 🕒 Date: 2
📌 PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks 🗂 Category: DEEP LEARNING 🕒 Date: 2025-09-24 | ⏱️ Read time: 15 min read Deep learning is shaping our world as we speak. In fact, it has been slowly…

📌 RAG Explained: Reranking for Better Answers 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-24 | ⏱️ Read time: 10 min
📌 RAG Explained: Reranking for Better Answers 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-24 | ⏱️ Read time: 10 min read How reranking improves retrieval-augmented generation by surfacing the most relevant results

📌 Decoding Nonlinear Signals In Large Observational Datasets 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-24 | ⏱️ Read tim
📌 Decoding Nonlinear Signals In Large Observational Datasets 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-09-24 | ⏱️ Read time: 28 min read Rain, snow, or something In between?

📌 Carving out your competitive advantage with AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-17 | ⏱️ Read time: 15
📌 Carving out your competitive advantage with AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-17 | ⏱️ Read time: 15 min read Why the future of AI isn’t just automation – It’s craftsmanship, strategy, and innovation

📌 What Does It Take to Get Your Foot in the Door as a Data Scientist? 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-17 | ⏱️ Re
📌 What Does It Take to Get Your Foot in the Door as a Data Scientist? 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-17 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

📌 Integrating Multimodal Data into a Large Language Model 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-10-17 | ⏱️ Read t
📌 Integrating Multimodal Data into a Large Language Model 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-10-17 | ⏱️ Read time: 18 min read Developing a context-retrieval, multimodal RAG using advanced parsing, semantic & keyword search, and re-ranking

📌 GraphMuse: A Python Library for Symbolic Music Graph Processing 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-17 | ⏱️ Read t
📌 GraphMuse: A Python Library for Symbolic Music Graph Processing 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-17 | ⏱️ Read time: 12 min read Yes, music and graphs do mix!

📌 Autoencoders: An Ultimate Guide for Data Scientists 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-17 | ⏱️ Read time: 25 min
📌 Autoencoders: An Ultimate Guide for Data Scientists 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-17 | ⏱️ Read time: 25 min read A beginner’s guide to the architecture, Python implementation, and a glimpse into the future

📌 Why You Should Be Hiring Methodologists 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-17 | ⏱️ Read time: 6 min read “All you
📌 Why You Should Be Hiring Methodologists 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-17 | ⏱️ Read time: 6 min read “All you need to do is develop your mind. If you have thought deeply, nearly…

📌 How to Export a Stata “Notebook” to HTML 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-17 | ⏱️ Read time: 9 min read Create a
📌 How to Export a Stata “Notebook” to HTML 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-17 | ⏱️ Read time: 9 min read Create a shareable HTML document with your code, outputs, and graphs

📌 Reinforcement Learning for Physics: ODEs and Hyperparameter Tuning 🗂 Category: PHYSICS 🕒 Date: 2024-10-17 | ⏱️ Read time
📌 Reinforcement Learning for Physics: ODEs and Hyperparameter Tuning 🗂 Category: PHYSICS 🕒 Date: 2024-10-17 | ⏱️ Read time: 13 min read Controlling differential equations with gymnasium and optimizing algorithm hyperparameters

📌 What are Digital Twins? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-18 | ⏱️ Read time: 7 min read Bridging the p
📌 What are Digital Twins? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-18 | ⏱️ Read time: 7 min read Bridging the physical and digital worlds