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Machine Learning

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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📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 346 subscribers, ranking 3 329 in the Technologies & Applications category and 225 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 346 subscribers.

According to the latest data from 11 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 393 over the last 30 days and by 17 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.29%. Within the first 24 hours after publication, content typically collects 1.74% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 924 views. Within the first day, a publication typically gains 702 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
  • Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
Real Machine Learning — 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 12 July, 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.

40 346
Subscribers
+1724 hours
+1237 days
+39330 days
Posts Archive
📌 Why You Should Not Replace Blanks with 0 in Power BI 🗂 Category: DATA ANALYSIS 🕒 Date: 2025-06-20 | ⏱️ Read time: 7 min
📌 Why You Should Not Replace Blanks with 0 in Power BI 🗂 Category: DATA ANALYSIS 🕒 Date: 2025-06-20 | ⏱️ Read time: 7 min read Did someone ask you to replace blank values with 0 in your reports? Maybe you…

📌 Building AI-Powered Low-Code Workflows with n8n 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-23 | ⏱️ Read time: 2
📌 Building AI-Powered Low-Code Workflows with n8n 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-23 | ⏱️ Read time: 27 min read Three powerful workflows that you can apply to your personal life or business today

📌 Building A Modern Dashboard with Python and Taipy 🗂 Category: PROGRAMMING 🕒 Date: 2025-06-23 | ⏱️ Read time: 11 min read
📌 Building A Modern Dashboard with Python and Taipy 🗂 Category: PROGRAMMING 🕒 Date: 2025-06-23 | ⏱️ Read time: 11 min read A guide to building a front-end data application.

📌 Programming, Not Prompting: A Hands-On Guide to DSPy 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-23 | ⏱️ Read ti
📌 Programming, Not Prompting: A Hands-On Guide to DSPy 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-23 | ⏱️ Read time: 16 min read A practical deep dive into declarative AI programming

📌 Reinforcement Learning from Human Feedback, Explained Simply 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-23 | ⏱️ R
📌 Reinforcement Learning from Human Feedback, Explained Simply 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-23 | ⏱️ Read time: 7 min read The one technique that made ChatGPT so smart

📌 Build Multi-Agent Apps with OpenAI’s Agent SDK 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-24 | ⏱️ Read time: 21
📌 Build Multi-Agent Apps with OpenAI’s Agent SDK 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-24 | ⏱️ Read time: 21 min read Creating multi-agent apps is simple with this open-source SDK, and it can be used with…

📌 Why Your Next LLM Might Not Have A Tokenizer 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-24 | ⏱️ Read time: 16 min
📌 Why Your Next LLM Might Not Have A Tokenizer 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-24 | ⏱️ Read time: 16 min read The Tokenizer Has Been a Necessary Evil, but This Radical Approach Shows That It Might…

📌 Agentic AI: Implementing Long-Term Memory 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-24 | ⏱️ Read time: 11 min
📌 Agentic AI: Implementing Long-Term Memory 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-24 | ⏱️ Read time: 11 min read The problem and current solutions

📌 Data Has No Moat! 🗂 Category: DATA SCIENCE 🕒 Date: 2025-06-24 | ⏱️ Read time: 7 min read Only if you ignore data quality
📌 Data Has No Moat! 🗂 Category: DATA SCIENCE 🕒 Date: 2025-06-24 | ⏱️ Read time: 7 min read Only if you ignore data quality

📌 Stop Chasing “Efficiency AI.” The Real Value Is in “Opportunity AI.” 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06
📌 Stop Chasing “Efficiency AI.” The Real Value Is in “Opportunity AI.” 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-25 | ⏱️ Read time: 11 min read Companies pursuing incremental productivity gains risk being displaced by AI-native competitors building entirely new business…

📌 How to Train a Chatbot Using RAG and Custom Data 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-25 | ⏱️ Read time: 6
📌 How to Train a Chatbot Using RAG and Custom Data 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-25 | ⏱️ Read time: 6 min read Retrieval-Augmented Generation made easy with Llama

📌 Economic Cycle Synchronization with Dynamic Time Warping 🗂 Category: ECONOMICS 🕒 Date: 2025-06-25 | ⏱️ Read time: 7 min
📌 Economic Cycle Synchronization with Dynamic Time Warping 🗂 Category: ECONOMICS 🕒 Date: 2025-06-25 | ⏱️ Read time: 7 min read The case of the Eurozone

📌 Use OpenAI Whisper for Automated Transcriptions 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-25 | ⏱️ Read time: 8 m
📌 Use OpenAI Whisper for Automated Transcriptions 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-25 | ⏱️ Read time: 8 min read Streamline your computer interactions using OpenAI’s Whisper model

📌 AI Agent with Multi-Session Memory 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-26 | ⏱️ Read time: 9 min read Bui
📌 AI Agent with Multi-Session Memory 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-26 | ⏱️ Read time: 9 min read Build from scratch using only Python & Ollama (no GPU, no APIKEY)

📌 The Mythical Pivot Point from Buy to Build for Data Platforms 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-06-26 | ⏱️ Read
📌 The Mythical Pivot Point from Buy to Build for Data Platforms 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-06-26 | ⏱️ Read time: 10 min read For companies with data-intensive architectures, there often comes a pivotal point where building in-house data…

📌 Data Science: From School to Work, Part V 🗂 Category: PROGRAMMING 🕒 Date: 2025-06-26 | ⏱️ Read time: 18 min read How to
📌 Data Science: From School to Work, Part V 🗂 Category: PROGRAMMING 🕒 Date: 2025-06-26 | ⏱️ Read time: 18 min read How to profile your Python project

📌 Hitchhiker’s Guide to RAG with ChatGPT API and LangChain 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-26 | ⏱️ Rea
📌 Hitchhiker’s Guide to RAG with ChatGPT API and LangChain 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-26 | ⏱️ Read time: 7 min read Build a simple Python RAG pipeline using your local files as context

📌 A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-06-2
📌 A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-06-26 | ⏱️ Read time: 16 min read PyTorch model performance analysis and optimization — Part 8

📌 Pipelining AI/ML Training Workloads with CUDA Streams 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-06-26 | ⏱️ Read time: 12
📌 Pipelining AI/ML Training Workloads with CUDA Streams 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-06-26 | ⏱️ Read time: 12 min read PyTorch Model Performance Analysis and Optimization — Part 9

📌 A Developer’s Guide to Building Scalable AI: Workflows vs Agents 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-27
📌 A Developer’s Guide to Building Scalable AI: Workflows vs Agents 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-27 | ⏱️ Read time: 38 min read A practical guide to choosing between AI agents and workflows for production systems, covering the…