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Machine Learning

Machine Learning

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

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πŸ“ˆ Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 149 subscribers, ranking 3 375 in the Technologies & Applications category and 227 in the Syria region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 40 149 subscribers.

According to the latest data from 28 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 378 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.09%. Within the first 24 hours after publication, content typically collects 1.91% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 841 views. Within the first day, a publication typically gains 766 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
  • Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œReal Machine Learning β€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 29 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

40 149
Subscribers
+724 hours
+1147 days
+37830 days
Posts Archive
πŸ“Œ EDA for Word Embeddings πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 16 min read Data scientists use EDA
πŸ“Œ EDA for Word Embeddings πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 16 min read Data scientists use EDA for everything. Why not word embeddings?

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

πŸ€–πŸ§  ROMA: The Ultimate AI Framework That Lets You Build High-Performance Agents in Minutes πŸ—“οΈ 11 Oct 2025 πŸ“š AI News & Tren
πŸ€–πŸ§  ROMA: The Ultimate AI Framework That Lets You Build High-Performance Agents in Minutes πŸ—“οΈ 11 Oct 2025 πŸ“š 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

πŸ“Œ A Practical Framework for Search Evaluation πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 15 min read A Da
πŸ“Œ A Practical Framework for Search Evaluation πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 15 min read A Data-Driven Approach to Elevating User Experience and Business Performance with Search

πŸ“Œ Multi-Headed Self Attention – By Hand πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 5 min read
πŸ“Œ Multi-Headed Self Attention – By Hand πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 5 min read Hand computing the cornerstone of modern AI.

πŸ“Œ Predictive Marketing Mix Modeling with GLOP: The Perfect Cocktail Shaker πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-07-12
πŸ“Œ Predictive Marketing Mix Modeling with GLOP: The Perfect Cocktail Shaker πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 12 min read Maximizing profit using ML and GLOP (by Google) in the digital landscape

πŸ“Œ Deliver Your Data as a Product, But Not as an Application πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-07-12 | ⏱️ Read time
πŸ“Œ Deliver Your Data as a Product, But Not as an Application πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 11 min read Data as a product is an intriguing concept, but beware of the application trap

πŸ€–πŸ§  How oLLM Makes Large-Context AI Models Run Smoothly on 8GB GPUs πŸ—“οΈ 11 Oct 2025 πŸ“š AI News & Trends Artificial intellige
πŸ€–πŸ§  How oLLM Makes Large-Context AI Models Run Smoothly on 8GB GPUs πŸ—“οΈ 11 Oct 2025 πŸ“š 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

πŸ“Œ Rainbow: The Colorful Evolution of Deep Q-Networks πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 20 min r
πŸ“Œ Rainbow: The Colorful Evolution of Deep Q-Networks πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 20 min read Everything you need to assemble the DQN Megazord in JAX.

πŸ“Œ Time Series Are Not That Different for LLMs πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 8 min
πŸ“Œ Time Series Are Not That Different for LLMs πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-12 | ⏱️ Read time: 8 min read Harnessing the power of LLMs for time series modeling

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

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

πŸ€–πŸ§  Gamma PPT AI : Unlock Presentations in Minutes πŸ—“οΈ 10 Oct 2025 πŸ“š AI News & Trends In today’s fast-paced world, creating
πŸ€–πŸ§  Gamma PPT AI : Unlock Presentations in Minutes πŸ—“οΈ 10 Oct 2025 πŸ“š 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

πŸ“Œ Why It Feels Impossible to Get a Data Science Job πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-13 | ⏱️ Read time:
πŸ“Œ Why It Feels Impossible to Get a Data Science Job πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-13 | ⏱️ Read time: 10 min read Reasons why the market is tough and what you can do about it

πŸ“Œ Reinforcement Learning, Part 5: Temporal-Difference Learning πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-13 | ⏱️
πŸ“Œ Reinforcement Learning, Part 5: Temporal-Difference Learning πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-13 | ⏱️ Read time: 18 min read Intelligently synergizing dynamic programming and Monte Carlo algorithms

πŸ“Œ Essential Considerations for Implementing Machine Learning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-13 | ⏱️ Read time: 8
πŸ“Œ Essential Considerations for Implementing Machine Learning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-13 | ⏱️ Read time: 8 min read Is your use case a viable ML product from a traditional ML and production perspective?

πŸ“Œ Three reasons why developers should use DuckDB πŸ—‚ Category: SQL πŸ•’ Date: 2024-07-14 | ⏱️ Read time: 5 min read Developers
πŸ“Œ Three reasons why developers should use DuckDB πŸ—‚ Category: SQL πŸ•’ 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…

πŸ€–πŸ§  PyMuPDF: The Ultimate Python Library for High-Performance PDF Processing πŸ—“οΈ 09 Oct 2025 πŸ“š AI News & Trends If you’re a
πŸ€–πŸ§  PyMuPDF: The Ultimate Python Library for High-Performance PDF Processing πŸ—“οΈ 09 Oct 2025 πŸ“š 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

πŸ“Œ LLM Agents Demystified πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-14 | ⏱️ Read time: 16 min read Hands-on ReAct
πŸ“Œ LLM Agents Demystified πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-14 | ⏱️ Read time: 16 min read Hands-on ReAct agent implementation with AdalFlow library

πŸ“Œ Chaining Pandas Operations: Strengths and Limitations πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 20 min
πŸ“Œ Chaining Pandas Operations: Strengths and Limitations πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-15 | ⏱️ Read time: 20 min read Learn when it’s worth chaining Pandas operations in pipes.