uk
Feedback
Machine Learning

Machine Learning

Відкрити в Telegram

Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Показати більше

📈 Аналітичний огляд Telegram-каналу Machine Learning

Канал Machine Learning (@machinelearning9) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 40 149 підписників, посідаючи 3 375 місце в категорії Технології та додатки та 227 місце у регіоні Сирія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 40 149 підписників.

За останніми даними від 28 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 378, а за останні 24 години на 7, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 2.09%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.91% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 841 переглядів. Протягом першої доби публікація в середньому набирає 766 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як distance, insidead, gpu, learning, degree.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Завдяки високій частоті оновлень (останні дані отримано 29 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.

40 149
Підписники
+724 години
+1147 днів
+37830 день
Архів дописів
📌 EDA for Word Embeddings 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-12 | ⏱️ Read time: 16 min read Data scientists use EDA
📌 EDA for Word Embeddings 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-12 | ⏱️ Read time: 16 min read Data scientists use EDA for everything. Why not word embeddings?

📌 Gower’s Distance for Mixed Categorical and Numerical Data 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-12 | ⏱️ Read time: 8
📌 Gower’s Distance for Mixed Categorical and Numerical Data 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-12 | ⏱️ Read time: 8 min read A distance measure for mixed data that can be used for clustering

🤖🧠 ROMA: The Ultimate AI Framework That Lets You Build High-Performance Agents in Minutes 🗓️ 11 Oct 2025 📚 AI News & Tren
🤖🧠 ROMA: The Ultimate AI Framework That Lets You Build High-Performance Agents in Minutes 🗓️ 11 Oct 2025 📚 AI News & Trends Artificial Intelligence continues to evolve at an unprecedented pace, with agent-based frameworks becoming increasingly important for tackling complex problems. ROMA (Recursive Open Meta-Agents) represents a significant leap forward in this space, providing developers and researchers with a hierarchical, flexible, and high-performance framework for building multi-agent AI systems. This article explores ROMA’s architecture, technical capabilities, practical ... #ROMA #AIFramework #MultiAgentSystems #ArtificialIntelligence #HighPerformanceAI #AgentBasedAI

📌 A Practical Framework for Search Evaluation 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-12 | ⏱️ Read time: 15 min read A Da
📌 A Practical Framework for Search Evaluation 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-12 | ⏱️ Read time: 15 min read A Data-Driven Approach to Elevating User Experience and Business Performance with Search

📌 Multi-Headed Self Attention – By Hand 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-12 | ⏱️ Read time: 5 min read
📌 Multi-Headed Self Attention – By Hand 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-12 | ⏱️ Read time: 5 min read Hand computing the cornerstone of modern AI.

📌 Predictive Marketing Mix Modeling with GLOP: The Perfect Cocktail Shaker 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-07-12
📌 Predictive Marketing Mix Modeling with GLOP: The Perfect Cocktail Shaker 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-07-12 | ⏱️ Read time: 12 min read Maximizing profit using ML and GLOP (by Google) in the digital landscape

📌 Deliver Your Data as a Product, But Not as an Application 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-07-12 | ⏱️ Read time
📌 Deliver Your Data as a Product, But Not as an Application 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-07-12 | ⏱️ Read time: 11 min read Data as a product is an intriguing concept, but beware of the application trap

🤖🧠 How oLLM Makes Large-Context AI Models Run Smoothly on 8GB GPUs 🗓️ 11 Oct 2025 📚 AI News & Trends Artificial intellige
🤖🧠 How oLLM Makes Large-Context AI Models Run Smoothly on 8GB GPUs 🗓️ 11 Oct 2025 📚 AI News & Trends Artificial intelligence has revolutionized the way we process information, analyze data, and automate complex tasks. With the rise of large language models (LLMs), AI capabilities have grown exponentially, enabling applications from natural language understanding to multimodal reasoning. However, running these models efficiently especially with massive context windows, remains a challenge due to their high memory ... #oLLM #LargeContextAI #AIGPU #MachineLearning #LLMs #AIOptimization

📌 Rainbow: The Colorful Evolution of Deep Q-Networks 🗂 Category: DEEP LEARNING 🕒 Date: 2024-07-12 | ⏱️ Read time: 20 min r
📌 Rainbow: The Colorful Evolution of Deep Q-Networks 🗂 Category: DEEP LEARNING 🕒 Date: 2024-07-12 | ⏱️ Read time: 20 min read Everything you need to assemble the DQN Megazord in JAX.

📌 Time Series Are Not That Different for LLMs 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-12 | ⏱️ Read time: 8 min
📌 Time Series Are Not That Different for LLMs 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-12 | ⏱️ Read time: 8 min read Harnessing the power of LLMs for time series modeling

📌 Lessons Learned as a Data Science Manager and Why I’m Moving Back to an Individual Contributor Role 🗂 Category: CAREER AD
📌 Lessons Learned as a Data Science Manager and Why I’m Moving Back to an Individual Contributor Role 🗂 Category: CAREER ADVICE 🕒 Date: 2024-07-13 | ⏱️ Read time: 11 min read The three questions I asked myself that helped me pick my career path

📌 How to Deliver Successful Data Science Consulting Projects 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-13 | ⏱️ Read time: 1
📌 How to Deliver Successful Data Science Consulting Projects 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-13 | ⏱️ Read time: 11 min read Key recommendations for how to succeed with data science consulting projects and build lasting client…

🤖🧠 Gamma PPT AI : Unlock Presentations in Minutes 🗓️ 10 Oct 2025 📚 AI News & Trends In today’s fast-paced world, creating
🤖🧠 Gamma PPT AI : Unlock Presentations in Minutes 🗓️ 10 Oct 2025 📚 AI News & Trends In today’s fast-paced world, creating high-impact presentations can be a tedious, time-consuming process especially when you need beautiful visuals, crisp content and consistent branding. That’s where Gamma PPT AI comes in. It’s a tool that promises to transform how we make slide decks by letting AI handle design, layout and content generation. In this blog, ... #GammaPPTAI #AIPresentations #PresentationTools #ArtificialIntelligence #DesignAutomation #SlideDeck

📌 Why It Feels Impossible to Get a Data Science Job 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-13 | ⏱️ Read time:
📌 Why It Feels Impossible to Get a Data Science Job 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-13 | ⏱️ Read time: 10 min read Reasons why the market is tough and what you can do about it

📌 Reinforcement Learning, Part 5: Temporal-Difference Learning 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-13 | ⏱️
📌 Reinforcement Learning, Part 5: Temporal-Difference Learning 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-13 | ⏱️ Read time: 18 min read Intelligently synergizing dynamic programming and Monte Carlo algorithms

📌 Essential Considerations for Implementing Machine Learning 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-13 | ⏱️ Read time: 8
📌 Essential Considerations for Implementing Machine Learning 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-13 | ⏱️ Read time: 8 min read Is your use case a viable ML product from a traditional ML and production perspective?

📌 Three reasons why developers should use DuckDB 🗂 Category: SQL 🕒 Date: 2024-07-14 | ⏱️ Read time: 5 min read Developers
📌 Three reasons why developers should use DuckDB 🗂 Category: SQL 🕒 Date: 2024-07-14 | ⏱️ Read time: 5 min read Developers often have to analyse data, e.g. assessing the impact of an outage. DuckDB is…

🤖🧠 PyMuPDF: The Ultimate Python Library for High-Performance PDF Processing 🗓️ 09 Oct 2025 📚 AI News & Trends If you’re a
🤖🧠 PyMuPDF: The Ultimate Python Library for High-Performance PDF Processing 🗓️ 09 Oct 2025 📚 AI News & Trends If you’re a Python developer working with PDF documents whether it’s for text extraction, data analysis conversion or annotation then you’ve likely encountered the limitations of traditional tools. That’s where PyMuPDF also known as fitz, shines. It’s a lightweight, high-performance Python library that enables comprehensive PDF manipulation with minimal dependencies and maximum flexibility. In this ... #PyMuPDF #PythonLibrary #PDFProcessing #TextExtraction #DataAnalysis #HighPerformance

📌 LLM Agents Demystified 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-14 | ⏱️ Read time: 16 min read Hands-on ReAct
📌 LLM Agents Demystified 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-07-14 | ⏱️ Read time: 16 min read Hands-on ReAct agent implementation with AdalFlow library

📌 Chaining Pandas Operations: Strengths and Limitations 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-15 | ⏱️ Read time: 20 min
📌 Chaining Pandas Operations: Strengths and Limitations 🗂 Category: DATA SCIENCE 🕒 Date: 2024-07-15 | ⏱️ Read time: 20 min read Learn when it’s worth chaining Pandas operations in pipes.