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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 40 277 subscribers, ranking 3 347 in the Technologies & Applications category and 227 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 277 subscribers.

According to the latest data from 07 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 352 over the last 30 days and by 21 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.23%. Within the first 24 hours after publication, content typically collects 1.88% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 896 views. Within the first day, a publication typically gains 758 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.

📝 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 08 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.

40 277
Subscribers
+2124 hours
+957 days
+35230 days
Posts Archive
📌 Maximum-Effiency Coding Setup 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-16 | ⏱️ Read time: 9 min read Learn how to be a mo
📌 Maximum-Effiency Coding Setup 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-16 | ⏱️ Read time: 9 min read Learn how to be a more efficient programmer #DataScience #AI #Python

Adakah anda merasakan analisis anda sentiasa kekurangan rangka kerja?Kami telah menubuhkan forum perbincangan mendalam yang memberi t Adakah anda merasakan analisis anda sentiasa kekurangan rangka kerja?Kami telah menubuhkan forum perbincangan mendalam yang memberi t Sponsored By WaybienAds

📌 Do You Smell That? Hidden Technical Debt in AI Development 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-15 | ⏱️ R
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📌 The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon 🗂 Category: PRODUCTIVITY
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📌 How to Run Coding Agents in Parallel 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-15 | ⏱️ Read time: 8 min read Get the most o
📌 How to Run Coding Agents in Parallel 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-15 | ⏱️ Read time: 8 min read Get the most out of Claude Code #DataScience #AI #Python

📌 When Shapley Values Break: A Guide to Robust Model Explainability 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-15
📌 When Shapley Values Break: A Guide to Robust Model Explainability 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-15 | ⏱️ Read time: 9 min read Shapley Values are one of the most common methods for explainability, yet they can be… #DataScience #AI #Python

🎁❗️TODAY FREE❗️🎁 Entry to our VIP channel is completely free today. Tomorrow it will cost $500! 🔥 JOIN 👇 https://t.me/+DB
🎁❗️TODAY FREE❗️🎁 Entry to our VIP channel is completely free today. Tomorrow it will cost $500! 🔥 JOIN 👇 https://t.me/+DBdNGbxImzgxMDBi https://t.me/+DBdNGbxImzgxMDBi https://t.me/+DBdNGbxImzgxMDBi

📌 Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries 🗂 Category: MACHINE LEARNING 🕒
📌 Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-14 | ⏱️ Read time: 15 min read Seeded topic modeling, integration with LLMs, and training on summarized data are the fresh parts… #DataScience #AI #Python

📌 Glitches in the Attention Matrix 🗂 Category: DEEP LEARNING 🕒 Date: 2026-01-14 | ⏱️ Read time: 13 min read A history of T
📌 Glitches in the Attention Matrix 🗂 Category: DEEP LEARNING 🕒 Date: 2026-01-14 | ⏱️ Read time: 13 min read A history of Transformer artifacts and the latest research on how to fix them #DataScience #AI #Python

Do you want to teach AI on real projects? In this #repository, there are 29 projects with Generative #AI,#MachineLearning, an
Do you want to teach AI on real projects? In this #repository, there are 29 projects with Generative #AI,#MachineLearning, and #Deep +Learning. With full #code for each one. This is pure gold: https://github.com/KalyanM45/AI-Project-Gallery 👉 https://t.me/CodeProgrammer

📌 What Is a Knowledge Graph — and Why It Matters 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-14 | ⏱️ Read time: 18 min read H
📌 What Is a Knowledge Graph — and Why It Matters 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-14 | ⏱️ Read time: 18 min read How structured knowledge became healthcare’s quiet advantage #DataScience #AI #Python

📌 Why Human-Centered Data Analytics Matters More Than Ever 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-14 | ⏱️ Read time: 8 m
📌 Why Human-Centered Data Analytics Matters More Than Ever 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-14 | ⏱️ Read time: 8 min read From optimizing metrics to designing meaning: putting people back into data-driven decisions #DataScience #AI #Python

📌 From ‘Dataslows’ to Dataflows: The Gen2 Performance Revolution in Microsoft Fabric 🗂 Category: DATA ENGINEERING 🕒 Date:
📌 From ‘Dataslows’ to Dataflows: The Gen2 Performance Revolution in Microsoft Fabric 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-13 | ⏱️ Read time: 8 min read Dataflows were (rightly?) considered “the slowest and least performant option” for ingesting data into Power… #DataScience #AI #Python

📌 An introduction to AWS Bedrock 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-13 | ⏱️ Read time: 13 min read The ho
📌 An introduction to AWS Bedrock 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-13 | ⏱️ Read time: 13 min read The how, why, what and where of Amazon’s LLM access layer #DataScience #AI #Python

⚡️ All cheat sheets for programmers in one place. There's a lot of useful stuff inside: short, clear tips on languages, techn
⚡️ All cheat sheets for programmers in one place. There's a lot of useful stuff inside: short, clear tips on languages, technologies, and frameworks. No registration required and it's free. https://overapi.com/ #python #php #Database #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS https://t.me/CodeProgrammer ⚡️

📌 How to Maximize Claude Code Effectiveness 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-13 | ⏱️ Read time: 9 min read Learn how
📌 How to Maximize Claude Code Effectiveness 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-13 | ⏱️ Read time: 9 min read Learn how to get the most out of agentic coding #DataScience #AI #Python

📌 Why Your ML Model Works in Training But Fails in Production 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-13 | ⏱️
📌 Why Your ML Model Works in Training But Fails in Production 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-13 | ⏱️ Read time: 8 min read Hard lessons from building production ML systems where data leaks, defaults lie, populations shift, and… #DataScience #AI #Python

📌 Under the Uzès Sun: When Historical Data Reveals the Climate Change 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-13 | ⏱️ Rea
📌 Under the Uzès Sun: When Historical Data Reveals the Climate Change 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-13 | ⏱️ Read time: 11 min read Longer summers, milder winters: analysis of temperature trends in Uzès, France, year after year. #DataScience #AI #Python

📌 Optimizing Data Transfer in Batched AI/ML Inference Workloads 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-12 | ⏱️ Read
📌 Optimizing Data Transfer in Batched AI/ML Inference Workloads 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-12 | ⏱️ Read time: 13 min read A deep dive on data transfer bottlenecks, their identification, and their resolution with the help… #DataScience #AI #Python

📌 When Does Adding Fancy RAG Features Work? 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-12 | ⏱️ Read time: 23 min re
📌 When Does Adding Fancy RAG Features Work? 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-12 | ⏱️ Read time: 23 min read Looking at the performance of different pipelines #DataScience #AI #Python