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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 40 221 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
📌 Calculating the Uncertainty Coefficient (Theil’s U) in Python 🗂 Category: PROBABILITY 🕒 Date: 2024-10-18 | ⏱️ Read time:
📌 Calculating the Uncertainty Coefficient (Theil’s U) in Python 🗂 Category: PROBABILITY 🕒 Date: 2024-10-18 | ⏱️ Read time: 5 min read A measure of correlation between discrete (categorical) variables

📌 All you need to know about Non-Inferiority Hypothesis Test 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-18 | ⏱️ Read time: 6
📌 All you need to know about Non-Inferiority Hypothesis Test 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-18 | ⏱️ Read time: 6 min read A non-inferiority test proves that a new treatment is not worse than the standard by…

📌 Implementing Anthropic’s Contextual Retrieval for Powerful RAG Performance 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-
📌 Implementing Anthropic’s Contextual Retrieval for Powerful RAG Performance 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-18 | ⏱️ Read time: 16 min read This article will show you how to implement the contextual retrieval idea proposed by Anthropic

📌 Implementing “Modular RAG” with Haystack and Hypster 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-18 | ⏱️ Read ti
📌 Implementing “Modular RAG” with Haystack and Hypster 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-18 | ⏱️ Read time: 13 min read Transforming RAG Systems into LEGO-like Reconfigurable Frameworks

📌 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?

📌 Evaluating Model Retraining Strategies 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-20 | ⏱️ Read time: 11 min read How d
📌 Evaluating Model Retraining Strategies 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-20 | ⏱️ Read time: 11 min read How data drift and concept drift matter to choose the right retraining strategy?

📌 Linked Lists – Data Structures & Algorithms for Data Scientists 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read ti
📌 Linked Lists – Data Structures & Algorithms for Data Scientists 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 6 min read How linked lists and queues work under the hood

📌 SQL and Data Modelling in Action: A Deep Dive into Data Lakehouses 🗂 Category: SQL 🕒 Date: 2024-10-21 | ⏱️ Read time: 12
📌 SQL and Data Modelling in Action: A Deep Dive into Data Lakehouses 🗂 Category: SQL 🕒 Date: 2024-10-21 | ⏱️ Read time: 12 min read Lakehouses as a continuation of data warehouses and data lakes. What is this architecture about?

📌 Efficient Document Chunking Using LLMs: Unlocking Knowledge One Block at a Time 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Da
📌 Efficient Document Chunking Using LLMs: Unlocking Knowledge One Block at a Time 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 9 min read This article explains how to use an LLM (Large Language Model) to perform the chunking…

📌 The Power of Optimization in Designing Experiments Involving Small Samples 🗂 Category: 🕒 Date: 2024-10-21 | ⏱️ Read time
📌 The Power of Optimization in Designing Experiments Involving Small Samples 🗂 Category: 🕒 Date: 2024-10-21 | ⏱️ Read time: 11 min read A step-by-step guide to designing more precise experiments using optimization in Python

📌 Don’t Do Laundry Today, It Will Be Cheaper Tomorrow 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 19 min r
📌 Don’t Do Laundry Today, It Will Be Cheaper Tomorrow 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 19 min read Analysing electricity price changes in London through causal inference

📌 Awesome Plotly with Code Series (Part 1): Alternatives to Bar Charts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Re
📌 Awesome Plotly with Code Series (Part 1): Alternatives to Bar Charts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 14 min read A bar chart is not always the best solution.

📌 OLAP is Dead – Or Is It ? 🗂 Category: ANALYTICS 🕒 Date: 2024-10-21 | ⏱️ Read time: 16 min read OLAP’s fate in the age of
📌 OLAP is Dead – Or Is It ? 🗂 Category: ANALYTICS 🕒 Date: 2024-10-21 | ⏱️ Read time: 16 min read OLAP’s fate in the age of modern analytics

📌 Unleash the Power of Probability to Predict the Future of Your Business 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️
📌 Unleash the Power of Probability to Predict the Future of Your Business 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-21 | ⏱️ Read time: 14 min read A Practical Guide to Applying Probability Concepts with Python in Real-World Contexts

📌 Discretization, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 |
📌 Discretization, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 10 min read 6 fun ways to categorize numbers into bins!

📌 Using Vector Steering to Improve Model Guidance 🗂 Category: 🕒 Date: 2024-10-22 | ⏱️ Read time: 10 min read Exploring the
📌 Using Vector Steering to Improve Model Guidance 🗂 Category: 🕒 Date: 2024-10-22 | ⏱️ Read time: 10 min read Exploring the Research on Vector Steering and Coding Up an Implementation

📌 Game Theory, Part 1 – The Prisoner’s Dilemma Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 7 min r
📌 Game Theory, Part 1 – The Prisoner’s Dilemma Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 7 min read Game theory is prevalent in real-life scenarios and decision-making

📌 Why Scaling Works: Inductive Biases vs The Bitter Lesson 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-22 | ⏱️ Rea
📌 Why Scaling Works: Inductive Biases vs The Bitter Lesson 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 11 min read Building deep insights with a toy problem

📌 Deep Learning vs Data Science: Who Will Win? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 14 min read Wha
📌 Deep Learning vs Data Science: Who Will Win? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 14 min read What is more important, your data or your model?

📌 Self-Service ML with Relational Deep Learning 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 8 m
📌 Self-Service ML with Relational Deep Learning 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-22 | ⏱️ Read time: 8 min read Do ML directly on your relational database