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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 40 208 subscribers, ranking 3 344 in the Technologies & Applications category and 228 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 40 208 subscribers.

According to the latest data from 03 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 338 over the last 30 days and by 9 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.04%. Within the first 24 hours after publication, content typically collects 2.42% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 822 views. Within the first day, a publication typically gains 973 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 04 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.

40 208
Subscribers
+924 hours
+727 days
+33830 days
Posts Archive
πŸ“Œ AI Agents: The Intersection of Tool Calling and Reasoning in Generative AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2
πŸ“Œ AI Agents: The Intersection of Tool Calling and Reasoning in Generative AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-05 | ⏱️ Read time: 13 min read Unpacking problem solving and tool-driven decision making in AI

πŸ“Œ How I Turned IPL Stats into a Mesmerizing Bar Chart Race πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-06 | ⏱️ Read time: 8 m
πŸ“Œ How I Turned IPL Stats into a Mesmerizing Bar Chart Race πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-06 | ⏱️ Read time: 8 min read A step-by-step guide to creating captivating animated visualizations for data storytelling

πŸ“Œ The Rise of Pallas: Unlocking TPU Potential with Custom Kernels πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-06 |
πŸ“Œ The Rise of Pallas: Unlocking TPU Potential with Custom Kernels πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-06 | ⏱️ Read time: 17 min read Accelerating AI/ML Model Training with Custom Operators – Part 3

πŸ“Œ FormulaFeatures: A Tool to Generate Highly Predictive Features for Interpretable Models πŸ—‚ Category: πŸ•’ Date: 2024-10-06 |
πŸ“Œ FormulaFeatures: A Tool to Generate Highly Predictive Features for Interpretable Models πŸ—‚ Category: πŸ•’ Date: 2024-10-06 | ⏱️ Read time: 41 min read Create more interpretable models by using concise, highly predictive features, automatically engineered based on arithmetic…

πŸ“Œ Exploring the AI Alignment Problem with GridWorlds πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-06 | ⏱️ Read time
πŸ“Œ Exploring the AI Alignment Problem with GridWorlds πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-06 | ⏱️ Read time: 25 min read It’s difficult to build capable AI agents without encountering orthogonal goals

πŸ“Œ How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs? πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-
πŸ“Œ How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs? πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-10-06 | ⏱️ Read time: 15 min read Delve into an end-to-end Machine Learning project to improve the quality of the Open Food…

πŸ“Œ Getting Started with Powerful Data Tables in your Python Web Apps πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-06 | ⏱️ Read
πŸ“Œ Getting Started with Powerful Data Tables in your Python Web Apps πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-06 | ⏱️ Read time: 6 min read Using AG Grid to build a Finance app in pure Python with Reflex

πŸ“Œ Top 5 Geospatial Data APIs for Advanced Analysis πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-06 | ⏱️ Read time: 22 min read
πŸ“Œ Top 5 Geospatial Data APIs for Advanced Analysis πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-06 | ⏱️ Read time: 22 min read Explore Overpass, Geoapify, Distancematrix.ai, Amadeus, and Mapillary for Advanced Mapping and Location Data

πŸ“Œ Arrays – Data Structures & Algorithms for Data Scientists πŸ—‚ Category: CODING πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 6 min re
πŸ“Œ Arrays – Data Structures & Algorithms for Data Scientists πŸ—‚ Category: CODING πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 6 min read How dynamic and static arrays work under the hood

πŸ“Œ Discover AWS Lambda Basics to Run Powerful Serverless Functions πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-07 |
πŸ“Œ Discover AWS Lambda Basics to Run Powerful Serverless Functions πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 12 min read Learn how I navigated setting up AWS Lambda for the first time

πŸ“Œ AlphaFold 2 Through the Context of BERT πŸ—‚ Category: πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 9 min read Understanding AI appli
πŸ“Œ AlphaFold 2 Through the Context of BERT πŸ—‚ Category: πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 9 min read Understanding AI applications in bio for machine learning engineers

πŸ“Œ Using Linear Equations + LLM to Solve LinkedIn Queens Game πŸ—‚ Category: πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 11 min read Pr
πŸ“Œ Using Linear Equations + LLM to Solve LinkedIn Queens Game πŸ—‚ Category: πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 11 min read Prompting GPT to form and solve the linear equations using PuLP

πŸ“Œ Scaling RAG from POC to Production πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 8 min read Common challenges a
πŸ“Œ Scaling RAG from POC to Production πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 8 min read Common challenges and architectural components to enable scaling

πŸ“Œ K Nearest Neighbor Regressor, Explained: A Visual Guide with Code Examples πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-07 |
πŸ“Œ K Nearest Neighbor Regressor, Explained: A Visual Guide with Code Examples πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 11 min read Finding the neighbors FAST with KD Trees and Ball Trees

πŸ“Œ Supercharge Your LLM Apps using DSPy and Langfuse πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2024-10-07 | ⏱️ Read t
πŸ“Œ Supercharge Your LLM Apps using DSPy and Langfuse πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 14 min read Build Production Grade LLM Apps with Ease

πŸ“Œ Implementing Sequential Algorithms on TPU πŸ—‚ Category: πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 13 min read Accelerating AI/ML
πŸ“Œ Implementing Sequential Algorithms on TPU πŸ—‚ Category: πŸ•’ Date: 2024-10-07 | ⏱️ Read time: 13 min read Accelerating AI/ML Model Training with Custom Operators – Part 3.A

πŸ“Œ How to Talk About Data and Analysis Simply πŸ—‚ Category: πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 21 min read So that it is unde
πŸ“Œ How to Talk About Data and Analysis Simply πŸ—‚ Category: πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 21 min read So that it is understandable and engaging to (almost) everyone

πŸ“Œ Pandora’s Cloud Migration: Conquer the 7 β€œBringers of Evil” πŸ—‚ Category: πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 20 min read A
πŸ“Œ Pandora’s Cloud Migration: Conquer the 7 β€œBringers of Evil” πŸ—‚ Category: πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 20 min read A guide to conquering cloud migration challenges

πŸ“Œ Adding Gradient Backgrounds to Plotly Charts πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 5 min read Usin
πŸ“Œ Adding Gradient Backgrounds to Plotly Charts πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 5 min read Using Plotly rectangle shapes to improve data visualisation

πŸ“Œ Precisely Compare Geographical Regions with GeoPandas πŸ—‚ Category: πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 9 min read Filling
πŸ“Œ Precisely Compare Geographical Regions with GeoPandas πŸ—‚ Category: πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 9 min read Filling maps with area measurements