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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 40 205 подписчиков, занимая 3 352 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 40 205 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.

40 205
Подписчики
+1024 часа
+837 дней
+34330 день
Архив постов
📌 Bayesian Linear Regression: A Complete Beginner’s guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-14 | ⏱️ Read time: 11 m
📌 Bayesian Linear Regression: A Complete Beginner’s guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-14 | ⏱️ Read time: 11 min read A workflow and code walkthrough for building a Bayesian regression model in STAN

📌 Build a Tokenizer for the Thai Language from Scratch 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-14 | ⏱️ Read ti
📌 Build a Tokenizer for the Thai Language from Scratch 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-14 | ⏱️ Read time: 16 min read A step-by-step guide to building a Thai multilingual sub-word tokenizer based on a BPE algorithm…

“I never thought I’d get access to the entire CEH v13 course… for FREE. Everyone told me, ‘That’s impossible!’ But now I’m al
“I never thought I’d get access to the entire CEH v13 course… for FREE. Everyone told me, ‘That’s impossible!’ But now I’m already learning secrets even pros don’t share. Curious what’s really inside? Check it out here before it disappears. #ad InsideAds

📌 A Powerful Feature for Boosting Python Code Efficiency and Streamlining Complex Workflows 🗂 Category: DATA SCIENCE 🕒 Dat
📌 A Powerful Feature for Boosting Python Code Efficiency and Streamlining Complex Workflows 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-15 | ⏱️ Read time: 10 min read All you need to know about Python loops

📌 Applications of Rolling Windows for Time Series, with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-15 | ⏱️ Read time:
📌 Applications of Rolling Windows for Time Series, with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-15 | ⏱️ Read time: 12 min read Here’s some powerful applications of Rolling Windows and Time Series

📌 Introducing NumPy, Part 3: Manipulating Arrays 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-15 | ⏱️ Read time: 7 min read Sh
📌 Introducing NumPy, Part 3: Manipulating Arrays 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-15 | ⏱️ Read time: 7 min read Shaping, transposing, joining, and splitting arrays

📌 OpenAI o1: Is This the Enigmatic Force That Will Reshape Every Knowledge Sector We Know? 🗂 Category: CHATGPT 🕒 Date: 202
📌 OpenAI o1: Is This the Enigmatic Force That Will Reshape Every Knowledge Sector We Know? 🗂 Category: CHATGPT 🕒 Date: 2024-09-16 | ⏱️ Read time: 7 min read My first encounters with the o1 model

📌 ASCVIT V1: Automatic Statistical Calculation, Visualization and Interpretation Tool 🗂 Category: DATA SCIENCE 🕒 Date: 202
📌 ASCVIT V1: Automatic Statistical Calculation, Visualization and Interpretation Tool 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 38 min read Automated data analysis made easy: The first version of ASCVIT the tool for statistical calculation,…

📌 What Makes a Great Data Business 🗂 Category: BUSINESS 🕒 Date: 2024-09-16 | ⏱️ Read time: 8 min read Including an easy-to
📌 What Makes a Great Data Business 🗂 Category: BUSINESS 🕒 Date: 2024-09-16 | ⏱️ Read time: 8 min read Including an easy-to-use data business evaluation cheat sheet

📌 Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide 🗂 Category: ARTIFICIAL INTELLIGENCE
📌 Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-10-01 | ⏱️ Read time: 18 min read Comparing model-free and model-based RL methods on a dynamic grid world

📌 GPU Accelerated Polars – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-17 |
📌 GPU Accelerated Polars – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 16 min read Fast Dataframes for Big Problems

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📌 Building RAGs Without A Retrieval Model Is a Terrible Mistake 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time
📌 Building RAGs Without A Retrieval Model Is a Terrible Mistake 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 10 min read Here are my favorite techniques – one is faster, the other is more accurate.

📌 The Mystery Behind the PyTorch Automatic Mixed Precision Library 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-17 | ⏱️ Read
📌 The Mystery Behind the PyTorch Automatic Mixed Precision Library 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-17 | ⏱️ Read time: 9 min read How to get 2X speed up model training using three lines of code

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Ever wonder how real traders grow $1,000 into proven profits—step by step, with full transparency? Elite Gold Trading opens the door to professional copytrading, verified results, and exclusive strategies you can follow today. New members get a 100% deposit bonus—start with a real edge from day one. Ready to see how the pros do it? Join now & claim your bonus before this offer ends! #ad InsideAds

📌 Introduction to Maximum Likelihood Estimates 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read time: 9 min read Lear
📌 Introduction to Maximum Likelihood Estimates 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read time: 9 min read Learn about Maximum Likelihood Estimates via their application for next-word prediction

📌 Unlocking Business Potential Through Effective Customer Segmentation 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Re
📌 Unlocking Business Potential Through Effective Customer Segmentation 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read time: 9 min read Transform your data into actionable insights with customer segmentation for improved engagement and profitability

📌 Asking for Feedback as a Data Scientist Individual Contributor 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read tim
📌 Asking for Feedback as a Data Scientist Individual Contributor 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-18 | ⏱️ Read time: 17 min read Receive clear and useful feedback. Ditch generic questions. More than 60 example questions for you…

📌 Under the Hood: How DAX Works with Filters 🗂 Category: POWER BI 🕒 Date: 2025-10-01 | ⏱️ Read time: 6 min read Have you e
📌 Under the Hood: How DAX Works with Filters 🗂 Category: POWER BI 🕒 Date: 2025-10-01 | ⏱️ Read time: 6 min read Have you ever wondered how DAX works with filters in Measures? Today, I take a…

📌 Visual Pollen Classification Using CNNs and Vision Transformers 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-10-01 | ⏱️ Rea
📌 Visual Pollen Classification Using CNNs and Vision Transformers 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-10-01 | ⏱️ Read time: 19 min read Filling the data gap: A machine learning approach to pollen identification in ecology and biotechnology