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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
📌 Let’s Analyze OpenAI’s Claims About ChatGPT Energy Use 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-16 | ⏱️ Read
📌 Let’s Analyze OpenAI’s Claims About ChatGPT Energy Use 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-16 | ⏱️ Read time: 7 min read ChatGPT uses an average of 0.34 Wh per query, according to a blog post by…

📌 Grad-CAM from Scratch with PyTorch Hooks 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-06-17 | ⏱️ Read time: 16 min read A h
📌 Grad-CAM from Scratch with PyTorch Hooks 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-06-17 | ⏱️ Read time: 16 min read A hands-on look at an explainable AI (XAI) technique that helps reveal why a convolutional…

📌 Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project 🗂 Category: DATA SCIENCE 🕒 Date:
📌 Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project 🗂 Category: DATA SCIENCE 🕒 Date: 2025-06-17 | ⏱️ Read time: 6 min read Three cases to use the Sphinx tool as a pro

📌 LLaVA on a Budget: Multimodal AI with Limited Resources 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-06-17 | ⏱️ Read time:
📌 LLaVA on a Budget: Multimodal AI with Limited Resources 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-06-17 | ⏱️ Read time: 8 min read Let’s get started with multimodality

📌 Abstract Classes: A Software Engineering Concept Data Scientists Must Know To Succeed 🗂 Category: DATA SCIENCE 🕒 Date: 2
📌 Abstract Classes: A Software Engineering Concept Data Scientists Must Know To Succeed 🗂 Category: DATA SCIENCE 🕒 Date: 2025-06-17 | ⏱️ Read time: 14 min read Simple concepts that differentiate a professional from amateurs.

📌 Computer Vision’s Annotation Bottleneck Is Finally Breaking 🗂 Category: SPONSORED CONTENT 🕒 Date: 2025-06-18 | ⏱️ Read t
📌 Computer Vision’s Annotation Bottleneck Is Finally Breaking 🗂 Category: SPONSORED CONTENT 🕒 Date: 2025-06-18 | ⏱️ Read time: 8 min read A Technical Deep Dive into Auto-Labeling

📌 Can We Use Chess to Predict Soccer? 🗂 Category: DATA SCIENCE 🕒 Date: 2025-06-18 | ⏱️ Read time: 29 min read An adaptatio
📌 Can We Use Chess to Predict Soccer? 🗂 Category: DATA SCIENCE 🕒 Date: 2025-06-18 | ⏱️ Read time: 29 min read An adaptation of Elo ratings for soccer implemented in Python

📌 A Multi-Agent SQL Assistant You Can Trust with Human-in-Loop Checkpoint & LLM Cost Control 🗂 Category: ARTIFICIAL INTELLI
📌 A Multi-Agent SQL Assistant You Can Trust with Human-in-Loop Checkpoint & LLM Cost Control 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-18 | ⏱️ Read time: 19 min read Your very own SQL assistant built with Streamlit, SQLite, & CrewAI

📌 Animating Linear Transformations with Quiver 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-06-18 | ⏱️ Read time: 8 min rea
📌 Animating Linear Transformations with Quiver 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-06-18 | ⏱️ Read time: 8 min read A useful tool in your quiver

📌 Beyond Code Generation: Continuously Evolve Text with LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-19 | ⏱️ Rea
📌 Beyond Code Generation: Continuously Evolve Text with LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-19 | ⏱️ Read time: 17 min read Long-running content evolution and an introduction to result analysis

📌 Core Machine Learning Skills, Revisited 🗂 Category: THE VARIABLE 🕒 Date: 2025-06-19 | ⏱️ Read time: 3 min read With all
📌 Core Machine Learning Skills, Revisited 🗂 Category: THE VARIABLE 🕒 Date: 2025-06-19 | ⏱️ Read time: 3 min read With all the buzz around agents, LLMs, and the tools they power, it’s sometimes easy…

📌 From Configuration to Orchestration: Building an ETL Workflow with AWS Is No Longer a Struggle 🗂 Category: DATA ENGINEERI
📌 From Configuration to Orchestration: Building an ETL Workflow with AWS Is No Longer a Struggle 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-06-19 | ⏱️ Read time: 7 min read A step-by-step guide to leverage AWS services for efficient data pipeline automation

📌 LLM-as-a-Judge: A Practical Guide 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-19 | ⏱️ Read time: 16 min read How t
📌 LLM-as-a-Judge: A Practical Guide 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-19 | ⏱️ Read time: 16 min read How to Scale LLM Evaluations Beyond Manual Review

📌 From Tokens to Theorems: Building a Neuro-Symbolic AI Mathematician 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-
📌 From Tokens to Theorems: Building a Neuro-Symbolic AI Mathematician 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-08 | ⏱️ Read time: 25 min read The next Gauss may not be born — they may be spun up in the…

📌 Agentic AI and the Future of Python Project Management Tooling 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-08 | ⏱️ Read time:
📌 Agentic AI and the Future of Python Project Management Tooling 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-08 | ⏱️ Read time: 10 min read Introducing a pyramid framework of evolution, accelerating and decelerating factors, and strategic recommendations for incumbents…

📌 Implementing the Gaussian Challenge in Python 🗂 Category: PROGRAMMING 🕒 Date: 2025-09-08 | ⏱️ Read time: 5 min read Begi
📌 Implementing the Gaussian Challenge in Python 🗂 Category: PROGRAMMING 🕒 Date: 2025-09-08 | ⏱️ Read time: 5 min read Beginner-friendly tutorial to understand range function and Python loops

📌 Understanding Matrices | Part 2: Matrix-Matrix Multiplication 🗂 Category: MATH 🕒 Date: 2025-06-19 | ⏱️ Read time: 15 min
📌 Understanding Matrices | Part 2: Matrix-Matrix Multiplication 🗂 Category: MATH 🕒 Date: 2025-06-19 | ⏱️ Read time: 15 min read The physical meaning of multiplying two matrices and how it works on several special matrices.

📌 Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work 🗂 Category: ARTIFICIAL INTELLIGEN
📌 Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-06-19 | ⏱️ Read time: 16 min read Transforming Independent Models into Collaborative Intelligence

📌 Understanding Application Performance with Roofline Modeling 🗂 Category: 🕒 Date: 2025-06-20 | ⏱️ Read time: 10 min read
📌 Understanding Application Performance with Roofline Modeling 🗂 Category: 🕒 Date: 2025-06-20 | ⏱️ Read time: 10 min read A common challenge with calculating an application’s performance is that the real-world performance and theoretical…

📌 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…