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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
Больше📈 Аналитический обзор Telegram-канала Machine Learning
Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 150 подписчиков, занимая 3 364 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 40 150 подписчиков.
Согласно последним данным от 27 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 412, а за последние 24 часа — 5, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 1.96%. В первые 24 часа после публикации контент обычно набирает 1.89% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 785 просмотров. В течение первых суток публикация набирает 760 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 2.
- 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 28 июня, 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.
40 150
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+1067 дней
+41230 день
Архив постов
40 150
📌 How I Used Machine Learning to Predict 41% of Project Delays Before They Happened
🗂 Category: PROJECT MANAGEMENT
🕒 Date: 2025-10-17 | ⏱️ Read time: 12 min read
How data science can help project managers anticipate risks and save time
40 150
📌 How to Classify Lung Cancer Subtype from DNA Copy Numbers Using PyTorch
🗂 Category: DEEP LEARNING
🕒 Date: 2025-10-17 | ⏱️ Read time: 12 min read
A step-by-step introduction to understanding cancer from the perspective of a data scientist.
40 150
📌 Machine Learning Meets Panel Data: What Practitioners Need to Know
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-10-17 | ⏱️ Read time: 8 min read
How to avoid overestimating machine learning models’ performance, usefulness, and real-world applicability due to hidden…
40 150
🤖🧠 Sora: OpenAI’s Breakthrough Text-to-Video Model Transforming Visual Creativity
🗓️ 18 Oct 2025
📚 AI News & Trends
Introduction Artificial Intelligence (AI) is rapidly transforming the creative world. From generating realistic images to composing music and writing code, AI has redefined how humans interact with technology. But one of the most revolutionary advancements in this domain is Sora, OpenAI’s text-to-video generative model that converts written prompts into hyper-realistic video clips. Ithas captured global ...
#Sora #OpenAI #TextToVideo #AI #VisualCreativity #GenerativeModel
40 150
🤖🧠 Sora: OpenAI’s Breakthrough Text-to-Video Model Transforming Visual Creativity
🗓️ 18 Oct 2025
📚 AI News & Trends
Introduction Artificial Intelligence (AI) is rapidly transforming the creative world. From generating realistic images to composing music and writing code, AI has redefined how humans interact with technology. But one of the most revolutionary advancements in this domain is Sora, OpenAI’s text-to-video generative model that converts written prompts into hyper-realistic video clips. Ithas captured global ...
#Sora #OpenAI #TextToVideo #AI #VisualCreativity #GenerativeModel
40 150
📌 Learnings from a Machine Learning Engineer — Part 2: The Data Sets
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-02-13 | ⏱️ Read time: 10 min read
In Part 1, we discussed the importance of collecting good image data and assigning proper labels…
40 150
📌 Learnings from a Machine Learning Engineer — Part 4: The Model
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-02-13 | ⏱️ Read time: 8 min read
In this latest part of my series, I will share what I have learned on…
40 150
📌 Learnings from a Machine Learning Engineer — Part 1: The Data
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-02-13 | ⏱️ Read time: 13 min read
It is said that in order for a machine learning model to be successful, you…
40 150
📌 Learnings from a Machine Learning Engineer — Part 3: The Evaluation
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-02-13 | ⏱️ Read time: 13 min read
In this third part of my series, I will explore the evaluation process which is…
40 150
📌 Learnings from a Machine Learning Engineer — Part 5: The Training
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-02-13 | ⏱️ Read time: 16 min read
In this fifth part of my series, I will outline the steps for creating a…
40 150
📌 Building a Data Engineering Center of Excellence
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-02-13 | ⏱️ Read time: 11 min read
As data continues to grow in importance and become more complex, the need for skilled…
40 150
Repost from Machine Learning with Python
AI Engineering roadmap that beginners can actually follow. Everything is based on 100% free, open-source, and community resources
All resources can be found here: GitHub
👉 @codeprogrammer
40 150
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Everyone’s missing out on the one-click PROXY that just saved my chats. See the link — unlock Telegram when you need it most!
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40 150
📌 Analyzing Lake Mendota Ice Phenology with Python
🗂 Category: CLIMATE CHANGE
🕒 Date: 2024-06-10 | ⏱️ Read time: 10 min read
An analysis-ready dataset you can use right now
40 150
📌 Task-Aware RAG Strategies for When Sentence Similarity Fails
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-10 | ⏱️ Read time: 19 min read
When retrieval is based on an additional metric besides just sentence similarity, traditional methods perform…
40 150
🤖🧠 Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents
🗓️ 17 Oct 2025
📚 AI News & Trends
AEPO (Agentic Entropy-Balanced Policy Optimization) represents a major advancement in the evolution of Agentic Reinforcement Learning (RL). As large language models (LLMs) increasingly act as autonomous web agents – searching, reasoning and interacting with tools – the need for balanced exploration and stability has become crucial. Traditional RL methods often rely heavily on entropy to ...
#AgenticRL #ReinforcementLearning #LLMs #WebAgents #EntropyBalanced #PolicyOptimization
40 150
📌 Spatial Index: Space-Filling Curves
🗂 Category: DATABASE DESIGN
🕒 Date: 2024-06-11 | ⏱️ Read time: 11 min read
Spatial Index and Space Filling Curves for Multi-dimensional data
40 150
📌 Reinforcement Learning, Part 4: Monte Carlo Control
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-11 | ⏱️ Read time: 17 min read
Harnessing Monte Carlo algorithms to discover the best strategies
40 150
📌 Anatomy of Windows Functions
🗂 Category: DATA ENGINEERING
🕒 Date: 2024-06-11 | ⏱️ Read time: 15 min read
Theory and practice of an underappreciated SQL operation
40 150
📌 SQL Explained: Ranking Analytics
🗂 Category: DATA ENGINEERING
🕒 Date: 2024-06-11 | ⏱️ Read time: 11 min read
What they are and how you use them
