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 Сирия228 Технологии и приложения3 344...

📈 Аналитический обзор Telegram-канала Machine Learning

Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 208 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 40 208 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.

40 208

Подписчики

+924 часа

+727 дней

+33830 день

822

Просмотры поста

~ 97324 часа

~ 1 32148 часов

2.04%

Коэффициент вовлеченности

~ 3

Постов в день

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Архив постов

40 209

📌 Nine Rules for Running Rust on WASM WASI 🗂 Category: PROGRAMMING 🕒 Date: 2024-09-28 | ⏱️ Read time: 16 min read Practical Lessons from Porting range-set-blaze to this Container-Like Environment

40 209

📌 Model Deployment with FastAPI, Azure, and Docker 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-28 | ⏱️ Read time: 11 min read A Complete Guide to Serving a Machine Learning Model with FastAPI

40 209

📌 Exploring the Link between Sleep Disorders and Health Indicators 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-28 | ⏱️ Read time: 16 min read A Python analysis of a MIMIC-IV health data (DREAMT) to uncover insights into factors affecting…

40 209

📌 Hands-On Optimization Using Genetic Algorithms, with Python 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-29 | ⏱️ Read time: 15 min read Here’s a full guide on genetic algorithms, what they are, and how to use them

40 209

📌 How to Get Pull Request Data Using GitHub API 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-29 | ⏱️ Read time: 5 min read Getting the diff between any two commits

40 209

📌 What’s Inside a Neural Network? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-29 | ⏱️ Read time: 5 min read Plotting surface of error in 3D using PyTorch

40 209

📌 To Mask or Not to Mask: The Effect of Prompt Tokens on Instruction Tuning 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-30 | ⏱️ Read time: 37 min read Implementing prompt-loss-weight, and why we should replace prompt-masking with prompt-weighting

40 209

📌 Eulerian Melodies: Graph Algorithms for Music Composition 🗂 Category: GRAPH THEORY 🕒 Date: 2025-09-28 | ⏱️ Read time: 15 min read Conceptual overview and an end-to-end Python implementation

40 209

Repost from Machine Learning with Python

🏳️‍🌈 Learning Python for science is ✅ with these 8 awesome GitHub repos! 🖥 Repo: Project Based Learning 💬 One of the most famous educational repos with 230K+ stars that implements various algorithms and projects using Python. ➖ ➖ ➖ 🖥 Repo: Real Python Materials 💬 Supplementary resources and exercises including project-based tutorials, guides, and practical exercises. ➖ ➖ ➖ 🖥 Repo: Learn By Doing 💬 Project-based tutorials in AI and machine learning for all levels. ➖ ➖ ➖ 🖥 Repo: Awesome Jupyter 💬 A curated collection of notebooks, tools, and powerful libraries for working with Jupyter. ➖ ➖ ➖ 🖥 Repo: Python Mini Projects 💬 A collection of mini-projects like games and small apps that you can quickly run and practice. ➖ ➖ ➖ 🖥 Repo: 100Projects of Code 💬 An educational challenge including 100 real projects; you practice and see your progress day by day. ➖ ➖ ➖ 🖥 Repo: Data Science Projects 💬 Practical ideas and examples to start data science with Python. ➖ ➖ ➖ 🖥 Repo: Python Project Scripts 💬 Small and large scripting projects, from beginner to advanced levels. By: https://t.me/CodeProgrammer ✈️

40 209

📌 The AI Developer’s Dilemma: Proprietary AI vs. Open Source Ecosystem 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-30 | ⏱️ Read time: 20 min read Fundamental Choices Impacting Integration and Deployment at Scale of GenAI into Businesses

40 209

📌 Evaluating Train-Test Split Strategies in Machine Learning: Beyond the Basics 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-30 | ⏱️ Read time: 6 min read Creating Appropriate Test Sets and Sleeping Soundly.

40 209

📌 Stein’s Paradox 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-30 | ⏱️ Read time: 8 min read Why the Sample Mean Isn’t Always the Best

40 209

📌 Is Less More? Do Deep Learning Forecasting Models Need Feature Reduction? 🗂 Category: ANALYTICS 🕒 Date: 2024-09-30 | ⏱️ Read time: 14 min read To curate, or not to curate, that is the question

40 209

📌 Exploring the World of Markov Chains: Unlocking the Power of Probabilistic Transitions 🗂 Category: PROBABILITY 🕒 Date: 2024-09-30 | ⏱️ Read time: 11 min read An Introduction to Markov Chains, their applications, and how to use Monte Carlo Simulations in…

40 209

📌 5 Must-Know Techniques for Mastering Time-Series Analysis 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-30 | ⏱️ Read time: 22 min read Elevate Your Machine Learning Forecasting with Accurate Data Splitting, Time-Series Cross-Validation, Feature Engineering, and More!

40 209

📌 Evaluating performance of LLM-based Applications 🗂 Category: 🕒 Date: 2024-09-30 | ⏱️ Read time: 9 min read Evaluation Framework for real-world requirements

40 209

📌 Can Transformers Solve Everything? 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-01 | ⏱️ Read time: 15 min read Looking into the math and the data reveals that transformers are both overused and underused.

40 209

📌 Support Vector Classifier, Explained: A Visual Guide with Mini 2D Dataset 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-01 | ⏱️ Read time: 17 min read Finding the best “line” to separate the classes? Yeah, sure…

40 209

📌 What I Learned in my First 9 Months as a Freelance Data Scientist 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-01 | ⏱️ Read time: 24 min read Observations and lessons learned from in the trenches

40 209

📌 Graph Neural Networks Part 1. Graph Convolutional Networks Explained 🗂 Category: 🕒 Date: 2024-10-01 | ⏱️ Read time: 12 min read Node classification with Graph Convolutional Networks