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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 40 202 подписчиков, занимая 3 365 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 40 202 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.

40 202
Подписчики
+1024 часа
+837 дней
+34330 день
Архив постов
📌 How I Used Clustering to Improve Chunking and Build Better RAGs 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-04 | ⏱️ Read ti
📌 How I Used Clustering to Improve Chunking and Build Better RAGs 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-04 | ⏱️ Read time: 8 min read It’s both fast and cost-effective

📌 Batch And Streaming Demystified For Unification 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-09-04 | ⏱️ Read time: 29 min r
📌 Batch And Streaming Demystified For Unification 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-09-04 | ⏱️ Read time: 29 min read Understand how batch can be considered a subset of streaming and why data engineering should…

📌 How to Train a Vision Transformer (ViT) from Scratch 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-04 | ⏱️ Read ti
📌 How to Train a Vision Transformer (ViT) from Scratch 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-04 | ⏱️ Read time: 13 min read A practical guide to implementing the Vision Transformer (ViT)

📌 Hands-On Global Optimization Methods, with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-04 | ⏱️ Read time: 15 min rea
📌 Hands-On Global Optimization Methods, with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-04 | ⏱️ Read time: 15 min read Four methods to find the maximum (or minimum) of your black box objective function

📌 Monte Carlo Methods for Solving Reinforcement Learning Problems 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-04 | ⏱️ Rea
📌 Monte Carlo Methods for Solving Reinforcement Learning Problems 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-04 | ⏱️ Read time: 20 min read Dissecting “Reinforcement Learning” by Richard S. Sutton with Custom Python Implementations, Episode III

📌 Automated Prompt Engineering: The Definitive Hands-On Guide 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-04 | ⏱️ Re
📌 Automated Prompt Engineering: The Definitive Hands-On Guide 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-04 | ⏱️ Read time: 26 min read Learn how to automate prompt engineering and unlock significant performance improvements in your LLM workload

📌 Understanding Time Series Structural Changes 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-04 | ⏱️ Read time: 7 mi
📌 Understanding Time Series Structural Changes 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-04 | ⏱️ Read time: 7 min read How to detect time series change points using Python

📌 Your Pathway to Success: How You Can Land a Machine Learning and Data Science Internship 🗂 Category: CAREER ADVICE 🕒 Dat
📌 Your Pathway to Success: How You Can Land a Machine Learning and Data Science Internship 🗂 Category: CAREER ADVICE 🕒 Date: 2024-09-04 | ⏱️ Read time: 18 min read Advice and tips from a data scientist who landed two internships in a year

📌 Peer Review Demystified: What, Why, and How 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-04 | ⏱️ Read time: 12 min read
📌 Peer Review Demystified: What, Why, and How 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-04 | ⏱️ Read time: 12 min read Learnings as an AI & Robotics Associate Editor with 100 Peer Reviews

📌 GPTs and the Forehead Detective 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-05 | ⏱️ Read time: 14 min read Are t
📌 GPTs and the Forehead Detective 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-05 | ⏱️ Read time: 14 min read Are the reasoning capabilities of OpenAI LLMs good enough to play the classic guessing game?

📌 My Weekly Calendar as a Senior Data Science Manager 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-05 | ⏱️ Read time: 17 min r
📌 My Weekly Calendar as a Senior Data Science Manager 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-05 | ⏱️ Read time: 17 min read My goal is to cover the 3Ps: People, Projects and Process. In that order of…

📌 The Latest on LLMs: Decision-Making, Knowledge Graphs, Reasoning Skills, and More 🗂 Category: DATA SCIENCE 🕒 Date: 2024-
📌 The Latest on LLMs: Decision-Making, Knowledge Graphs, Reasoning Skills, and More 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-05 | ⏱️ Read time: 5 min read Our weekly selection of must-read Editors’ Picks and original features

📌 How to Make an Advanced Spider Chart in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-05 | ⏱️ Read time: 8 min read St
📌 How to Make an Advanced Spider Chart in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-05 | ⏱️ Read time: 8 min read Step-by-step explanation with an easy to use function at the end

📌 Image Segmentation With K-Means Clustering 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-05 | ⏱️ Read time: 11 min read A
📌 Image Segmentation With K-Means Clustering 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-05 | ⏱️ Read time: 11 min read An introduction with Python

📌 An Introduction to Bayesian A/B Testing 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-05 | ⏱️ Read time: 7 min read Gain
📌 An Introduction to Bayesian A/B Testing 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-05 | ⏱️ Read time: 7 min read Gain better insights from your data

📌 How to Create a Custom Matplotlib Theme and Make Your Charts Go from Boring to Amazing 🗂 Category: DATA SCIENCE 🕒 Date:
📌 How to Create a Custom Matplotlib Theme and Make Your Charts Go from Boring to Amazing 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-05 | ⏱️ Read time: 6 min read The best part? You’ll only have to do this once.

📌 Building a Multilingual Multi-Agent Chat Application Using LangGraph – Part I 🗂 Category: 🕒 Date: 2024-09-06 | ⏱️ Read t
📌 Building a Multilingual Multi-Agent Chat Application Using LangGraph – Part I 🗂 Category: 🕒 Date: 2024-09-06 | ⏱️ Read time: 12 min read In this 3-part series, learn how to build a RAG-based, multilingual, agentic chat application to…

📌 Reasoning as the Engine Driving Legal Arguments 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-06 | ⏱️ Read time: 1
📌 Reasoning as the Engine Driving Legal Arguments 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-06 | ⏱️ Read time: 12 min read Statements of reasoning indicate types of argument

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📌 A Guide to Clustering Algorithms 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-06 | ⏱️ Read time: 6 min read An overview of c
📌 A Guide to Clustering Algorithms 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-06 | ⏱️ Read time: 6 min read An overview of clustering and the different families of clustering algorithms.